Merge pull request #83 from wiltodelta/chore/text-preservation-evaluation

Add verified text restoration
2026-08-19 20:17:12 +02:00 · 2026-08-15 12:30:27 -07:00
parent f8003f505a 432b63b6d7
commit 2369d351a9
35 changed files with 2582 additions and 98 deletions
@@ -199,6 +199,19 @@ image came from an AI generator, add `--force`:
 remove-ai-watermarks invisible image.png -o clean.png --force
 ```

+Typography-heavy images can opt into the experimental verified-text post-pass.
+It requires manually reviewed strings and line boxes; it never trusts OCR as ground
+truth or runs automatically:
+
+```bash
+uv tool install --force "remove-ai-watermarks[text-restoration]"
+remove-ai-watermarks invisible image.png -o clean.png \
+  --text-manifest verified-lines.json --force
+```
+
+See the [CLI guide](docs/cli.md#restore-operator-verified-text) for the manifest
+schema, compatibility restrictions, and oracle caveats.
+
 See the [installation guide](docs/installation.md) for Homebrew, uv, optional
 features, and development setup.

@@ -7,31 +7,394 @@ ground truth and instructions.

 | Original | Provider | Content | Exercises |
 | --- | --- | --- | --- |
+| `ChatGPT Image May 31, 2026, 02_02_23 PM.png` | OpenAI | Light multilingual typography | Text preservation |
 | `ChatGPT Image May 31, 2026, 02_03_55 PM.png` | OpenAI | Multilingual typography | Text preservation |
 | `Gemini_Generated_Image_633uuy633uuy633u.png` | Google | Landscape with a Chinese sign | CJK text preservation |
 | `Gemini_Generated_Image_y48j3cy48j3cy48j.png` | Google | Portrait grid | Face identity and skin texture |

 ## Text ground truth

-`ground-truth.json` contains hand-verified OCR for the two text-bearing
+`ground-truth.json` contains hand-verified OCR for the three text-bearing
 originals. To regenerate an OCR seed:

 ```bash
 uv run scripts/fidelity_metrics.py ocr \
  "data/synthid/originals/ChatGPT Image May 31, 2026, 02_03_55 PM.png" \
+  "data/synthid/originals/ChatGPT Image May 31, 2026, 02_02_23 PM.png" \
  data/synthid/originals/Gemini_Generated_Image_633uuy633uuy633u.png \
  --langs en,ru,ch \
  --out data/evaluations/fidelity/ground-truth.json
 ```

 Verify and correct the generated text by hand before using it as ground truth.
+`text-lines.json` contains the verified per-line strings and source-space boxes
+used by the evaluation-only selective-restoration experiment. It is not an
+automatic scene-text annotation set.
+
+`scripts/infer_text_lines.py` can generate a draft from source pixels without
+existing annotations. On the two posters it proposed 20 and 18 lines at the
+default threshold, but exact-text precision was only 90.0% and 94.4% because
+high-confidence OCR still dropped an English comma and replaced a Chinese comma
+with ASCII. Its output therefore requires manual verification of every line;
+`accepted` means crop-stable, not ground-truth-correct.
+
+## Text-preservation benchmark
+
+`text-preservation-2026-08-13.csv` records a fixed-seed comparison of the two
+current profiles and a global Z-Image Turbo prototype on all three text
+fixtures. Each candidate ran through the
+complete `visible -> invisible -> metadata` route with its profile default
+strength and adaptive-polish setting. The Z-Image prototype instead sweeps
+0.08, 0.10, 0.15, 0.20, and 0.25 without polish because its provider-specific
+removal floor is not known. The output hash identifies the exact bytes
+measured; generated outputs remain outside the repository.
+
+The character-weighted added CER is 0.262 for `qwen-zimage` and 0.256 for
+`sdxl-zimage`. That 0.006 absolute difference is not a
+stable ordering: SDXL wins the light poster, Qwen wins the dark poster, and the
+Chinese sign is tied. A paired sign test on the two non-ties is 1-1 (`p=1.0`).
+The measured sample therefore does not support a general text-preservation
+winner. Both profiles substantially degrade the smallest multilingual poster
+text and preserve the larger Chinese sign.
+
+Z-Image Turbo is the clear fidelity lead. At strength 0.10 its
+character-weighted CER is 0.093, against the unchanged sources' 0.124 OCR
+floor, while whole-image LPIPS stays between 0.047 and 0.086. Visual inspection
+still finds substitutions in the smallest Cyrillic and Chinese poster lines,
+so a favorable OCR score does not mean pixel-exact text. Quality drops quickly
+above 0.15; at 0.25 the weighted CER is 0.428.
+
+Provider-oracle checks on 2026-08-13 bracket the OpenAI requirement at no more
+than 0.10 for this sample. Both original OpenAI controls were detected by
+`openai.com/verify`; the light poster was clean at 0.08, while the dark poster
+was detected at 0.08 and clean at 0.10. The light 0.10 output was not separately
+checked. Google fails the entire tested sweep. The original Gemini image was
+detected through C2PA, and a pixel-identical copy with AI metadata stripped was
+separately detected by Gemini's built-in SynthID verifier. Z-Image outputs at
+0.08, 0.10, 0.15, 0.20, and 0.25 were all still detected. At 0.25 the weighted
+CER has already risen to 0.428, worse than the raw weighted CER of both current
+profiles (Qwen 0.387, SDXL 0.381). Increasing strength beyond the measured grid
+would therefore no longer serve the text-preservation objective without a new
+mechanism or hypothesis. Z-Image is not a viable global replacement on this
+evidence: no clean Google operating point was found before it lost its fidelity
+advantage.
+
+Qwen-Image-2.0 was not added to the numeric comparison. Its weights are not
+published, and its hosted edit API exposes an editing instruction and seed but
+no low-strength denoise control. It can be evaluated as a separate hosted edit
+strategy when credentials are available, but it is not a drop-in replacement
+for the partial-regeneration mechanism measured here.
+
+The OCR floor is the source image scored against the hand-verified text. Use
+`added_cer = text_cer - ocr_floor` when interpreting pipeline damage, because the
+unchanged poster sources already score 0.127 CER. `oracle_rechecked=true` marks
+the exact Z-Image bytes checked above; the remaining rows were not rechecked.
+The table does not certify other seeds, content classes, or strengths beyond
+the recorded provider verdicts.
+
+## Text-restoration prototype
+
+`text-restoration-2026-08-13.csv` evaluates an OCR-driven post-pass on the exact
+Qwen outputs above. The prototype recognizes English and Russian with macOS
+Vision and CJK with PaddleOCR, derives glyph masks independently from the
+source and Qwen output, removes both sets of glyphs with block-wise LaMa, and
+draws the recognized strings with new system-font pixels. It never composites
+source pixels back into the result.
+
+On the two multilingual posters, character-weighted CER fell from 0.338 and
+0.305 to 0.007 on both. OpenAI Verify reported no OpenAI signals for both Qwen
+controls and both restored outputs in the same run. The improvement comes with
+a substantial whole-image fidelity cost: LPIPS rose from 0.107 to 0.174 and
+from 0.095 to 0.162, while PSNR fell by about 10.5-11.4 dB. Visual inspection
+found one residual shadow in the smallest English line of the light poster;
+the dark poster was clean but the substitute fonts visibly changed typography.
+
+The Chinese sign did not improve: CER rose from 0.074 to 0.111 because the OCR
+and renderer changed punctuation. The Gemini verifier returned detected for
+both the restored output and its byte-identical Qwen control on a third work
+account, although that Qwen hash had previously returned clean on another work
+account. This run therefore does not isolate a restoration-stage regression;
+its Google verdict is inconclusive until a source-positive, Qwen-negative
+control can be reproduced in the same available account.
+
+This prototype is not ready to ship. Its strongest result establishes that
+fresh-glyph reconstruction can recover literal text without reintroducing an
+OpenAI signal, but portable OCR, font/style reconstruction, a tighter mask, and
+a reproducible Google oracle control remain prerequisites.
+
+### Selective restoration follow-up
+
+`selective-text-restoration-2026-08-13.csv` compares that full compositor with
+a selective prototype on the same two posters. The prototype leaves a Qwen line
+unchanged when padded source and output recognition agree, and applies the same
+LaMa plus fresh-system-font reconstruction only to lines whose recognized text
+changed. This reduced the edited area from 15.4% and 17.0% to 5.5% on both
+posters.
+
+Under one consistent Paddle `en+ru+ch` measurement route, selective restoration
+reduced Qwen CER from 0.378 to 0.101 on the light poster and from 0.413 to 0.112
+on the dark poster. Its image LPIPS was 0.120 and 0.103, substantially closer to
+Qwen than the full compositor's 0.174 and 0.162. These CER values must not be
+mixed with the preceding table's Vision/Paddle hybrid values: the comparison
+file remeasures all three variants through Paddle so their relative result is
+valid on one OCR route.
+
+OpenAI Verify returned `No OpenAI signals detected` for both selective outputs,
+then detected the original light poster as `Generated with OpenAI tools` in the
+same Chrome sequence. Visual inspection found the dark output clean, but the
+light output still retained a local shadow around one replaced fine-text line.
+Selective restoration is therefore the strongest current direction, not a
+production-ready default. The next implementation needs automatic line matching
+and a tighter source-plus-candidate glyph mask before it can be proposed for the
+pipeline.
+
+A mask-only follow-up added two pixels of dilation around every selected glyph.
+It visually removed the light poster's shadow and improved its CER from 0.101 to
+0.081 with effectively unchanged LPIPS, but increased the edited area from 5.5%
+to 7.0%. Applying the same rule to the dark poster worsened CER from 0.112 to
+0.147. These expanded-mask rows were not resubmitted to the provider oracle.
+The opposing result rules out one global dilation constant and makes per-line
+residual detection the next fidelity gate.
+
+An automatic follow-up removed the hand-selected line list. For each known
+source line, it runs a recognition-only model on padded, upscaled source and
+Qwen crops. It preserves a line only when both confidence scores are at least
+0.75, the source recognition is within 0.25 normalized edit distance of the
+verified line, and normalized source and candidate recognition match exactly.
+This reproduced the manual dark-poster line selection and additionally kept one
+correct large Russian line on the light poster.
+
+The automatic prototype also replaced uniform dilation with a second measured
+LaMa pass. After the first erase, it extracts contrast remaining at the original
+glyph positions, dilates only that residual, and inpaints it before drawing fresh
+text. Visual inspection found no remaining double glyphs on either output. The
+effective edited fractions were 4.1% and 5.7%. Image LPIPS was 0.113 and 0.104,
+against 0.120 and 0.103 for the manual selective version. Detector-based page CER
+was 0.123 and 0.119; the light value is inflated by the detector splitting the
+preserved Cyrillic line into Latin-like fragments even though the padded Russian
+recognizer reads it exactly. This is another reason that line decisions use
+recognition-only crops rather than the page detector's aggregate CER.
+
+Both exact automatic output hashes returned `No OpenAI signals detected`, and
+the original light poster returned `Generated with OpenAI tools` immediately
+afterward in the same Chrome sequence. The tracked script reproduced the dark
+hash exactly. Its light output differed from the initial prototype in only 386
+pixels by at most one channel value because separate ONNX environments rounded
+differently; the script's exact `900def5a...` hash was therefore submitted and
+also returned clean before the positive source control.
+
+The script and its required verified line annotations are tracked as
+`scripts/selective_text_restoration.py` and `text-lines.json`. For example:
+
+```bash
+PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK=True \
+  uv run scripts/selective_text_restoration.py \
+  data/synthid/originals/Gemini_Generated_Image_633uuy633uuy633u.png \
+  path/to/qwen-candidate.png \
+  --restoration vae-glyphs \
+  --glyph-donor path/to/qwen-vae-reconstruction.png \
+  --glyph-feather 0.5 \
+  --output path/to/selective-output.png \
+  --manifest path/to/manifest.json
+```
+
+The restoration mode is deliberately explicit. `vae-glyphs` requires a
+separately generated VAE reconstruction in `--glyph-donor`; it takes exact
+thresholded glyph cores from that donor, uses a fresh silhouette beneath them,
+and defaults to a narrow 0.5-pixel donor edge. The donor and the resulting exact bytes still require
+full-pipeline oracle evaluation. `rerender` reproduces the tracked
+fresh-system-font experiment. `source-glyphs` is only for compositing a
+separately regenerated, oracle-evaluated layer; feeding it the watermarked
+original would paste provenance-bearing pixels back into the result. The
+`--keep-background --composite-mask boxes` combination is an aligned-layer
+experiment and is not a production text restorer.
+
+The `source-silhouette` follow-up keeps the source glyph geometry but discards
+its pixel amplitudes: it thresholds each source line to a binary shape, samples
+one foreground color, and synthesizes new antialiasing over the scrubbed image.
+On a 24-image matrix spanning serif, Latin-diacritic, Cyrillic, CJK, tiny-UI,
+and rotated text, the original outer-feather compositor preserved all 34
+source-readable lines exactly under crop OCR, with median text-box SSIM 0.859.
+An inner-antialias variant raised median text-box SSIM to 0.902 and left
+whole-image SSIM unchanged at 0.672, but preserved 33/34 lines: OCR read one
+middle dot as a colon. The higher-fidelity antialiasing is retained in the
+evaluation script, with that punctuation miss recorded as a caveat. Its exact
+outputs were not submitted to the provider oracle because the
+public verifier reached its request limit; a clean verdict from the earlier
+outer-feather bytes does not transfer to the new hashes.
+
+Follow-up visual review on a typography-rich control rejected both silhouette
+compositors despite their OCR and SSIM scores. They preserved literal content
+and approximate glyph geometry, but changed stroke weight, color variation,
+edge antialiasing, and small decorative details enough to be plainly different
+from the source. OCR exactness and text-box SSIM are therefore screening metrics,
+not acceptance gates for source-typography preservation. A candidate must also
+retain source-like edge pixels and pass direct visual comparison at native size.
+
+A narrower Qwen-VAE donor follow-up keeps the scrubbed background, then copies
+only VAE-reconstructed pixels through the source-silhouette mask with a
+0.5-pixel feather. Across the 48-case typography matrix (548 annotated lines),
+text-box SSIM improved in 47/48 cases and glyph-edge error improved in 48/48.
+Median text-box SSIM rose from 0.854 to 0.914, while median glyph-edge MAE fell
+from 37.59 to 32.42. The median nonzero alpha area was 3.62%; on dense or large
+text this is still substantially wider than the target control. These are
+fidelity results only; the 48 matrix outputs were not submitted to the provider
+oracle. On a separate untracked dense-typography control, the exact core-only
+Qwen-VAE donor with a 1.43% source-silhouette area returned `No OpenAI signals
+detected`. Its 0.5-pixel feathered sibling, which raises the nonzero donor area
+to 2.75%, returned the same verdict and improved mean text-box SSIM from 0.957
+to 0.960. Crop OCR recovered from 7/15 exact lines on the raw pass to 14/15,
+matching the source OCR floor. Whole-image LPIPS was 0.082, but only 0.108% of
+pixels were exactly unchanged and the detected face retained 0.670 of source
+Laplacian variance. The tracked script reproduced the feathered file byte for
+byte. These two exact-byte verdicts do not certify other images or the larger
+matrix masks, and the global smoothing fails a strict unchanged-image criterion.
+
+The opt-in production port was rechecked separately on 2026-08-15. Its current
+LaMa runtime did not reproduce the earlier evaluation PNG byte for byte, but all
+changed pixels were confined to the erased background outside the donor glyph
+core. The exact production artifact returned `No OpenAI signals detected` in
+3/3 OpenAI Verify runs, while the matched source control returned `Generated
+with OpenAI tools` in 2/2 runs in the same Chrome session; expanded details
+identified SynthID and no C2PA manifest on the control. The private control and
+artifact hashes remain outside the public repository. This certifies only that
+runtime, verified manifest, and output, not arbitrary text masks or images.
+
+The Google result is negative. On the synthetic CJK sign case, two separate
+work-account runs both detected SynthID in the resaved source control and in the
+exact Qwen-VAE donor output. The candidate improved mean text-box SSIM from
+0.791 to 0.835 and glyph-edge MAE from 35.48 to 22.49 with a 3.67% donor area,
+but Gemini still detected it. The intermediate Qwen silhouette base and the
+earlier original-sign Qwen baseline were also detected in the same account.
+This isolates the current blocker upstream of text restoration: the global
+Google pass did not reach the SynthID removal floor, so the OpenAI-clean
+`vae-glyphs` configuration cannot be used as a Google operating point.
+
+On the large Chinese-sign control, source and candidate recognition agreed on
+all three lines. The script selected no changed lines, emitted a zero mask, and
+copied the candidate byte for byte instead of re-encoding it. This corrects the
+earlier unnecessary CJK rerender and proves the no-edit branch. It does not add
+a Google-negative oracle result: the available Google account still cannot
+reproduce a source-positive, Qwen-negative control sequence.
+
+The earlier automatic rerender was the first variant in the experiment to pass
+the recorded visual, fidelity, and OpenAI-oracle gates on its two poster
+fixtures. The later typography-rich control shows that result does not
+generalize to source-typography preservation. It remains evaluation-only: it
+depends on verified source text and source line boxes, uses macOS system fonts,
+and has not been validated on natural scene text, rotated text, or automatic
+line-box discovery.
+
+An opt-in `--detect-boxes` follow-up tested automatic geometry. Grouping Paddle
+word detections by vertical overlap found exactly 20/20, 20/20, and 3/3 lines;
+mean IoU with verified boxes was 0.857, 0.847, and 1.000. Reusing the annotation
+crop padding was unstable and preserved only 4/20 dark-poster lines. Reducing
+vertical recognition padding to 10% restored the exact 8/20 and 7/20 selection
+decisions, but detector CER was 0.127 and 0.154 rather than 0.123 and 0.119. The
+dark regression failed the fidelity gate, so these hashes were not submitted to
+the provider oracle. The flag is retained only to reproduce that negative
+evaluation and still requires verified strings and an exact line-count match.
+
+### AnyText2 glyph-conditioned follow-up
+
+`anytext2-restoration-2026-08-13.csv` tests the official Apache-2.0
+AnyText2 checkpoint as a local text-editing pass over the exact Qwen Chinese-sign
+output. The checkpoint hash was verified against ModelScope. Its own edit example
+successfully replaced a masked blackboard line with the requested `DADDY`, so the
+runtime and checkpoint were functional before the tracked fixture was measured.
+
+AnyText2 failed the fidelity gate. The standard full-image detector scored the
+default edit at CER 0.185 and the source-font mimic at 0.222, against 0.074 for
+Qwen; image LPIPS moved from 0.289 to 0.338 and 0.345. A padded crop-recognition
+cross-check removed the detector's punctuation misses: Qwen scored 0.000, the
+default edit still scored 0.074 with two substituted characters, and the mimic
+edit stayed at 0.222. Visual inspection agreed, with additional line-shape and
+punctuation errors in the mimic output. No provider-oracle submission was made
+because neither candidate preserved the requested content better than its
+already-clean Qwen input.
+
+This result covers the published SD1.5 checkpoint, three Chinese lines of no more
+than 20 characters, one seed, and one fixture. The repository still describes
+AnyText2XL as unreleased, and the inference wrapper truncates each requested line
+after 20 characters, so the published model cannot directly cover the longer
+English and Russian poster lines.
+
+The padded cross-check also changes the interpretation of this fixture's earlier
+restoration result. Its Qwen image already contains all three correct Chinese
+lines; the reported 0.074 came from the detector cropping the final punctuation
+from two lines. A deterministic rerender with horizontally padded recognition
+and a mask-matched Hiragino Sans GB W6 font reached detector CER 0.000, but image
+LPIPS worsened from 0.289 to 0.327 and the font was visibly heavier. The correct
+policy on this large-text fixture is therefore to leave a Qwen line unchanged
+when padded recognition confirms the source and candidate strings agree.
+
+## Face-stage isolation
+
+`face-stage-isolation-2026-08-13.csv` separates the two stages on the 18-face
+Gemini portrait-grid fixture. All variants use the same visible-cleaned,
+metadata-stripped input and seed 0. The prepared input remained positive in the
+Gemini SynthID verifier, so the surrounding visible and metadata stages did not
+create the clean verdicts.
+
+| Route | Gemini oracle | ID cosine | Face LPIPS | Laplacian ratio | Image LPIPS |
+| --- | --- | ---: | ---: | ---: | ---: |
+| Prepared control | detected | - | - | - | - |
+| Z-Image face stage only | detected | 0.927 | 0.028 | 0.612 | 0.013 |
+| Qwen global stage only | clean | 0.589 | 0.217 | 0.331 | 0.288 |
+| Full Qwen then Z-Image | clean | 0.852 | 0.050 | 0.492 | 0.252 |
+
+This isolates the roles: the Qwen global pass removes the watermark, while the
+masked Z-Image pass restores face identity and detail without reintroducing a
+detectable whole-image signal. Z-Image face-only does not remove SynthID. The
+result explains why the face stage succeeded while global Z-Image failed: the
+earlier clean verdict belonged to the two-stage pipeline, not to Z-Image as a
+standalone remover. This is one fixture, one seed, and one oracle pass per
+variant; it does not establish a general reintroduction threshold for mask size.
+
+## Upstream Synthid-Bypass v2 reproduction
+
+`upstream-v2-reproduction-2026-08-13.csv` records a source-level audit and a
+close reproduction of the public
+[`Synthid-Bypass-v2.0.json`](https://github.com/cebeuq/Synthid-Bypass/blob/3007d0351596ae0a78b7074dae7ad179710b1e48/Synthid-Bypass-v2.0.json).
+The audited workflow is SHA-256
+`41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6` at
+upstream commit `3007d0351596ae0a78b7074dae7ad179710b1e48`.
+
+Tracing links backward from `SaveImage` confirms that the active global stage is
+Qwen-Image-2512 Q4 with the Lightning four-step LoRA at 0.8, DiffSynth Canny at
+1.0, AuraFlow shift 3, `dpmpp_2m` plus `sgm_uniform`, CFG 1, and four steps. Its
+resolution helper uses adaptive level 6 over 0.08..0.15, which resolves to the
+0.154 ceiling for the 2816x1536 portrait fixture. Z-Image Turbo is used only by
+the face detailer: eight steps, CFG 1, `res_2s` plus `bong_tangent`, a 768 px
+guide, 1024 px cap, and direct adaptive denoise
+`clamp(0.10 * largest_face_ratio / 0.03, 0.05, 0.28)`. The active face regions
+come from YOLOv8-face plus SAM. Although the README describes MediaPipe as part
+of the strict path, the MediaPipe nodes do not reach the saved output. The
+1.2-megapixel scaler is also bypassed (`mode=4`).
+
+The published upstream pair 12 changed from SynthID-positive to clean in the
+Gemini verifier and scored 0.975 face identity. On the project portrait fixture,
+the prepared control was positive, while both the close global reproduction and
+the close full reproduction were clean. The global stage scored 0.589 identity
+and 0.217 face LPIPS; the upstream-strength face stage improved those to 0.783
+and 0.083. The maintained profile's weaker face pass scored 0.852 and 0.050 on
+the same control, so copying upstream's roughly doubled face strength would be a
+quality regression on this fixture.
+
+The close reproduction is intentionally labeled rather than presented as an
+exact ComfyUI run. It uses full-precision DiffSynth bf16 weights instead of the
+Q4 GGUF files, the maintained DiffSynth Lightning scheduler approximation
+instead of the ComfyUI sampler pair, YuNet plus SAM instead of YOLOv8-face plus
+SAM, and fixed seed 0 instead of randomized seeds. The two independent oracle
+controls and the published upstream pair establish the direction of the result;
+they do not make the local output byte-equivalent to upstream.

 ## Compare

 ```bash
 uv run scripts/fidelity_metrics.py compare \
  --original data/synthid/originals/Gemini_Generated_Image_y48j3cy48j3cy48j.png \
-  --variant controlnet=<out>.png \
-  --variant qwen=<out>.png \
+  --variant qwen-zimage=<qwen-out>.png \
+  --variant sdxl-zimage=<sdxl-out>.png \
  --ocr-langs ""
 ```
@@ -0,0 +1,4 @@
+date,source_commit,fixture,variant,model,model_revision,model_sha256,ocr_route,text_cer,img_lpips,ssim,psnr_db,output_sha256,provider_oracle,notes
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage,,,,"Paddle ch",0.074,0.289,0.696,23.3,4b05489c7a79f993d35d32528de792baad5a3e33ee950afa147caf5d3e0621,not_run,Exact Qwen baseline reused from text-restoration benchmark
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage-anytext2-default,AnyText2-SD1.5,b06c583a583818f3679665ef67b51363f107853c,7d5d593928d842500e0da101ab6a1ae28a8d21403441ca62c813c4f92529ace8,"Paddle ch",0.185,0.338,0.642,18.2,87729fe7e0482d2cead6d3b7c7dd386ecc75ab3d8658131c54e67528b8f9feeb,not_run,Fidelity gate failed; padded crop recognition CER 0.074 still shows two substituted characters; locally composited three-line edit; 20 DDIM steps; seed 0
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage-anytext2-mimic,AnyText2-SD1.5,b06c583a583818f3679665ef67b51363f107853c,7d5d593928d842500e0da101ab6a1ae28a8d21403441ca62c813c4f92529ace8,"Paddle ch",0.222,0.345,0.644,17.9,f5198d8f2365b79880aba8ea6b0c3885142b9de21c9f7f4f6712ac4a0db8dd85,not_run,Fidelity gate failed; source glyph masks supplied as font hints; 20 DDIM steps; seed 0
@@ -0,0 +1,5 @@
+date,source_commit,fixture,route,seed,global_strength,face_strength,face_count,id_cos,face_lpips,lapvar_ratio,img_lpips,ssim,psnr_db,output_sha256,gemini_oracle
+2026-08-13,2d03a00,Gemini_Generated_Image_y48j3cy48j3cy48j.png,visible-metadata-control,0,,,18,,,,,,,9f67264beb1da66a710885464a11f2ae85989094149f6145521d302cd2db940b,synthid_detected
+2026-08-13,2d03a00,Gemini_Generated_Image_y48j3cy48j3cy48j.png,zimage-face-only,0,,0.06604512532552083,18,0.927,0.028,0.612,0.013,0.959,34.7,c388e9bbf737bb9b22a7cb2216ffb8026321ca632f9db29e1e456b7c14a8553f,synthid_detected
+2026-08-13,2d03a00,Gemini_Generated_Image_y48j3cy48j3cy48j.png,qwen-global-only,0,0.153999996,,18,0.589,0.217,0.331,0.288,0.646,24.2,281a1d28d786d157cbbd13d00a32b215de4dfb3174a3c5e4e83c843e5402138f,clean
+2026-08-13,2d03a00,Gemini_Generated_Image_y48j3cy48j3cy48j.png,qwen-zimage-full,0,0.153999996,0.06604512532552083,18,0.852,0.050,0.492,0.252,0.671,24.7,69de191bddbfbf53348c7638bdba661b6d45d257c00a8a2646b2558670c5a4c2,clean
@@ -1,4 +1,5 @@
 {
+  "ChatGPT Image May 31, 2026, 02_02_23 PM.png": "This is a longer sample text in English.\nTypography can flow smoothly from large to medium size.\nSmaller lines help demonstrate hierarchy, rhythm, and clarity.\nEven the finest text should remain clean and readable.\n这是一段较长的中文示例文本。\n排版可以从大字号逐渐过渡到中字号。\n更小的文字能够展示层次、节奏与清晰度。\n即使是最小的一行，也应该保持清楚易读。\nЭто более длинный пример текста на русском языке.\nТипографика может плавно переходить от крупного размера к среднему.\nБолее мелкие строки показывают иерархию, ритм и ясность.\nДаже самый маленький текст должен оставаться чистым и читаемым.",
  "ChatGPT Image May 31, 2026, 02_03_55 PM.png": "This is a longer sample text in English.\nTypography can flow smoothly from large to medium size.\nSmaller lines help demonstrate hierarchy, rhythm, and clarity.\nEven the finest text should remain clean and readable.\n这是一段较长的中文示例文本。\n排版可以从大字号逐渐过渡到中字号。\n更小的文字能够展示层次、节奏与清晰度。\n即使是最小的一行，也应该保持清楚易读。\nЭто более длинный пример текста на русском языке.\nТипографика может плавно переходить от крупного размера к среднему.\nБолее мелкие строки показывают иерархию, ритм и ясность.\nДаже самый маленький текст должен оставаться чистым и читаемым.",
  "Gemini_Generated_Image_633uuy633uuy633u.png": "每天都是一个新的机会。\n用微笑开始它。\n世界也会向你微笑。"
 }
@@ -0,0 +1,14 @@
+date,source_commit,fixture,variant,ocr_route,text_cer,img_lpips,ssim,psnr_db,mask_fraction,output_sha256,provider_oracle,oracle_control
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage,"Paddle en+ru+ch",0.378,0.107,0.832,24.4,,bed68cf79a023cad582a11b41c4c2962bd90a218037ce931a2b3a8697e3bf4bd,no_openai_signals_detected,source-positive control established earlier
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-restored,"Paddle en+ru+ch",0.048,0.174,0.703,13.9,0.154,3221b2694f39420d509dd37cdaf69581432f9a189e87397346de0c78762402c6,no_openai_signals_detected,qwen-zimage clean in same run
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-selective,"Paddle en+ru+ch",0.101,0.120,0.796,19.5,0.054799,15b40b8cdf9e97558c66d4acdcb97d46769e64f04f143f8061746bc731fbbc21,no_openai_signals_detected,source detected in same browser sequence
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-selective-expanded-mask,"Paddle en+ru+ch",0.081,0.119,0.796,19.5,0.070267,5a8a0bcbe5763d1b1af0d64149cec7e35b16c5751e995d3bacf17581d64177ec,not_checked,fidelity-only follow-up
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-selective-automatic,"Paddle detector en+ru+ch; padded recognizer selection",0.123,0.113,0.808,21.2,0.041279,900def5a433c039aa474789eddc4dc4c63b8126ba1aff6829d2e8d600b9f9c71,no_openai_signals_detected,source detected immediately afterward in the same Chrome sequence
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage,"Paddle en+ru+ch",0.413,0.095,0.818,26.0,,83e777ad28bf80a8e5aa7d2e6ef2348337c667212e2c71075460352ad7423713,no_openai_signals_detected,source-positive control established earlier
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-restored,"Paddle en+ru+ch",0.086,0.162,0.682,14.6,0.170,95812399424feb70fc56d3e518d1ae0a3a50a3c2049d5902b00686ede9d3022d,no_openai_signals_detected,qwen-zimage clean in same run
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-selective,"Paddle en+ru+ch",0.112,0.103,0.780,20.8,0.054912,846f39db0ad4a48a734a5019f0b0ff5d110eecb3210223407e72529dfa4fcf54,no_openai_signals_detected,source detected in same browser sequence
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-selective-expanded-mask,"Paddle en+ru+ch",0.147,0.104,0.779,20.8,0.072556,8ae76e1dedeec26bebb8d35e71fd92d77f142dbb0f3440b330cdf33b79558afe,not_checked,fidelity-only follow-up
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-selective-automatic,"Paddle detector en+ru+ch; padded recognizer selection",0.119,0.104,0.779,20.8,0.057110,ac4201bd2e342b1a37737319438cedb2203f6bb58cd9113f825cf9b80d913755,no_openai_signals_detected,source detected in same browser sequence
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage-text-selective-automatic-passthrough,"Paddle detector ch; horizontal-only padded recognizer selection",0.074,0.289,0.696,23.3,0.000000,4b05489c7a79f993d35d32528de792baad5a3e33ee950afa147caf5d3e0621a6,not_checked,all three source and candidate recognitions agreed exactly; output is byte-identical to candidate
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-selective-detected-boxes,"Paddle ch detector boxes; 0.10-height recognizer padding",0.127,0.113,0.805,20.9,0.041280,70c52fed8b8a1d98f4cda8558ae08825313e510dfb372e448370d8dcf1da73b0,not_checked,fidelity-only automatic-box follow-up; same 8/20 preserve decisions
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-selective-detected-boxes,"Paddle ch detector boxes; 0.10-height recognizer padding",0.154,0.104,0.778,20.8,0.057146,1fa01ce706042c003a61429d5d97fc6c928ec32278071a91926d588f1e760b00,not_checked,fidelity-only automatic-box follow-up; same 7/20 preserve decisions but worse CER
@@ -0,0 +1,51 @@
+{
+  "ChatGPT Image May 31, 2026, 02_02_23 PM.png": [
+    {"box": [156, 206, 709, 296], "text": "This is a longer", "script": "alphabetic"},
+    {"box": [157, 293, 939, 379], "text": "sample text in English.", "script": "alphabetic"},
+    {"box": [165, 393, 774, 434], "text": "Typography can flow smoothly from", "script": "alphabetic"},
+    {"box": [161, 434, 521, 477], "text": "large to medium size.", "script": "alphabetic"},
+    {"box": [165, 495, 566, 522], "text": "Smaller lines help demonstrate hierarchy,", "script": "alphabetic"},
+    {"box": [165, 524, 354, 550], "text": "rhythm, and clarity.", "script": "alphabetic"},
+    {"box": [165, 564, 448, 585], "text": "Even the finest text should remain", "script": "alphabetic"},
+    {"box": [165, 593, 330, 613], "text": "clean and readable.", "script": "alphabetic"},
+    {"box": [158, 673, 903, 740], "text": "这是一段较长的中文示例文本。", "script": "cjk"},
+    {"box": [162, 753, 697, 793], "text": "排版可以从大字号逐渐过渡到中字号。", "script": "cjk"},
+    {"box": [164, 807, 524, 829], "text": "更小的文字能够展示层次、节奏与清晰度。", "script": "cjk"},
+    {"box": [163, 845, 483, 868], "text": "即使是最小的一行，也应该保持清楚易读。", "script": "cjk"},
+    {"box": [162, 951, 780, 1009], "text": "Это более длинный пример", "script": "alphabetic"},
+    {"box": [161, 1019, 723, 1062], "text": "текста на русском языке.", "script": "alphabetic"},
+    {"box": [165, 1076, 639, 1104], "text": "Типографика может плавно переходить от", "script": "alphabetic"},
+    {"box": [165, 1111, 505, 1133], "text": "крупного размера к среднему.", "script": "alphabetic"},
+    {"box": [165, 1151, 513, 1172], "text": "Более мелкие строки показывают иерархию,", "script": "alphabetic"},
+    {"box": [167, 1178, 287, 1192], "text": "ритм и ясность.", "script": "alphabetic"},
+    {"box": [165, 1210, 417, 1227], "text": "Даже самый маленький текст должен", "script": "alphabetic"},
+    {"box": [165, 1235, 381, 1247], "text": "оставаться чистым и читаемым.", "script": "alphabetic"}
+  ],
+  "ChatGPT Image May 31, 2026, 02_03_55 PM.png": [
+    {"box": [165, 204, 754, 299], "text": "This is a longer", "script": "alphabetic"},
+    {"box": [167, 295, 1006, 385], "text": "sample text in English.", "script": "alphabetic"},
+    {"box": [171, 401, 831, 446], "text": "Typography can flow smoothly from", "script": "alphabetic"},
+    {"box": [169, 446, 560, 491], "text": "large to medium size.", "script": "alphabetic"},
+    {"box": [173, 507, 605, 536], "text": "Smaller lines help demonstrate hierarchy,", "script": "alphabetic"},
+    {"box": [173, 538, 379, 567], "text": "rhythm, and clarity.", "script": "alphabetic"},
+    {"box": [175, 583, 481, 603], "text": "Even the finest text should remain", "script": "alphabetic"},
+    {"box": [173, 611, 352, 632], "text": "clean and readable.", "script": "alphabetic"},
+    {"box": [170, 693, 956, 757], "text": "这是一段较长的中文示例文本。", "script": "cjk"},
+    {"box": [174, 775, 740, 812], "text": "排版可以从大字号逐渐过渡到中字号。", "script": "cjk"},
+    {"box": [175, 829, 561, 852], "text": "更小的文字能够展示层次、节奏与清晰度。", "script": "cjk"},
+    {"box": [174, 869, 534, 892], "text": "即使是最小的一行，也应该保持清楚易读。", "script": "cjk"},
+    {"box": [169, 958, 824, 1022], "text": "Это более длинный пример", "script": "alphabetic"},
+    {"box": [171, 1029, 764, 1074], "text": "текста на русском языке.", "script": "alphabetic"},
+    {"box": [175, 1090, 678, 1119], "text": "Типографика может плавно переходить от", "script": "alphabetic"},
+    {"box": [175, 1125, 546, 1151], "text": "крупного размера к среднему.", "script": "alphabetic"},
+    {"box": [175, 1169, 552, 1192], "text": "Более мелкие строки показывают иерархию,", "script": "alphabetic"},
+    {"box": [175, 1196, 312, 1217], "text": "ритм и ясность.", "script": "alphabetic"},
+    {"box": [175, 1233, 479, 1253], "text": "Даже самый маленький текст должен", "script": "alphabetic"},
+    {"box": [177, 1259, 440, 1272], "text": "оставаться чистым и читаемым.", "script": "alphabetic"}
+  ],
+  "Gemini_Generated_Image_633uuy633uuy633u.png": [
+    {"box": [1281, 650, 2357, 818], "text": "每天都是一个新的机会。", "script": "cjk"},
+    {"box": [1466, 788, 2167, 957], "text": "用微笑开始它。", "script": "cjk"},
+    {"box": [1367, 910, 2264, 1112], "text": "世界也会向你微笑。", "script": "cjk"}
+  ]
+}
@@ -0,0 +1,25 @@
+date,source_commit,fixture,profile,route,seed,strength,adaptive_polish,text_cer,ocr_floor,added_cer,img_lpips,ssim,psnr_db,output_sha256,oracle_rechecked
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",original,reference,,,,0.127,0.127,0.000,0.000,1.000,inf,,not_applicable
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage,visible-invisible-metadata,0,0.11020972541176471,false,0.378,0.127,0.251,0.107,0.832,24.4,bed68cf79a023cad582a11b41c4c2962bd90a218037ce931a2b3a8697e3bf4bd,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",sdxl-zimage,visible-invisible-metadata,0,0.15,true,0.356,0.127,0.229,0.125,0.707,25.3,9bb932d6e66b52f34ff07c3fcdbdb9c33eb601ae2cd84bec6b8c7b5e30df9926,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",original,reference,,,,0.127,0.127,0.000,0.000,1.000,inf,,not_applicable
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage,visible-invisible-metadata,0,0.11020972541176471,false,0.413,0.127,0.286,0.095,0.818,26.0,83e777ad28bf80a8e5aa7d2e6ef2348337c667212e2c71075460352ad7423713,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",sdxl-zimage,visible-invisible-metadata,0,0.15,true,0.422,0.127,0.295,0.099,0.754,25.9,7605b1175b0c99f481ae6929acc488f34d98bc0f2571d42a1d2972b671d9b8cb,false
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,original,reference,,,,0.037,0.037,0.000,0.000,1.000,inf,,not_applicable
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage,visible-invisible-metadata,0,0.153999996,false,0.074,0.037,0.037,0.289,0.696,23.3,4b05489c7a79f993d35d32528de792baad5a3e33ee950afa147caf5d3e0621a6,false
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,sdxl-zimage,visible-invisible-metadata,0,0.25,true,0.074,0.037,0.037,0.176,0.769,26.1,d9f89465c31bd2d38ffb111775b9bbea4577f40713fcbebf421e05d344ab27ad,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",zimage-global,visible-invisible-metadata,0,0.08,false,0.068,0.127,-0.059,0.042,0.874,31.4,534de6422a3a03cfde44377aa9bf824a8267db3b73511240f8f07fb8dcb95eff,true
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",zimage-global,visible-invisible-metadata,0,0.10,false,0.086,0.127,-0.041,0.047,0.862,30.8,4dba8211e8d84ebbfff72de00f54d4365d0d202394acd412d1419ae93d1e9f71,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",zimage-global,visible-invisible-metadata,0,0.15,false,0.177,0.127,0.050,0.065,0.838,29.1,fc45c5fff6d33f15d30e05dd02580fe0addd97098b735b2894015f9abdaa43a7,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",zimage-global,visible-invisible-metadata,0,0.20,false,0.248,0.127,0.121,0.093,0.813,27.3,1e55e11f0e289a752098370e9f1d53fed6cd4fab7ceff46fe86dd20c927f9ffb,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",zimage-global,visible-invisible-metadata,0,0.25,false,0.343,0.127,0.216,0.120,0.787,25.7,f5a2d2829e88a790cff5bedc50fca1f878a92cdb392d530afb49d0d95c23b884,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",zimage-global,visible-invisible-metadata,0,0.08,false,0.143,0.127,0.016,0.042,0.877,31.5,5cfa466e276ab37cf532f7458d6dd86ea4f0807e2cbdb6788b7c01ca6ee35a1a,true
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",zimage-global,visible-invisible-metadata,0,0.10,false,0.101,0.127,-0.026,0.047,0.864,30.9,ade869ec7c5b08098495d9b9d67ee900773f189f94bbb411b043f242f5c2fa37,true
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",zimage-global,visible-invisible-metadata,0,0.15,false,0.208,0.127,0.081,0.060,0.839,29.3,a11976963e3f2ca856ce66278e22064d51345d449fa6d672029039a79517d60a,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",zimage-global,visible-invisible-metadata,0,0.20,false,0.341,0.127,0.214,0.070,0.819,27.6,1ab1ae075565e9d39960a80cf5d61e5b0c50d7e23283493ea0277056b3e964ab,false
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",zimage-global,visible-invisible-metadata,0,0.25,false,0.538,0.127,0.411,0.084,0.799,25.6,5ff5a364d84f738de4c56ead7d4950a2f9a1aaf4bec16e97bda6a65915744a57,false
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,zimage-global,visible-invisible-metadata,0,0.08,false,0.074,0.037,0.037,0.068,0.865,30.8,81419eddc3e5150956859dd10c26dd841bb93d9c8b34c01f344e6d69bca6edbf,true
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,zimage-global,visible-invisible-metadata,0,0.10,false,0.074,0.037,0.037,0.086,0.844,29.8,4d1f32bd0dac0e05885ff943e7c10230b06de7d8547e6215f8e2941f732a1df5,true
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,zimage-global,visible-invisible-metadata,0,0.15,false,0.000,0.037,-0.037,0.135,0.793,27.6,526c7987b34896f9513f7439b63b8a87573b59d15930fa307f3bb924435f3cec,true
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,zimage-global,visible-invisible-metadata,0,0.20,false,0.000,0.037,-0.037,0.179,0.746,25.9,325364cf7a3b5d39ac0c44af82181753ca4d6fde0f1b522c896414191725e8fc,true
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,zimage-global,visible-invisible-metadata,0,0.25,false,0.000,0.037,-0.037,0.218,0.703,24.5,f01d64dc0cabd45d66a93c1be39b819b988f29cda1f0a7bc0d5a1954571d807e,true
@@ -0,0 +1,7 @@
+date,source_commit,fixture,variant,ocr_route,text_cer,img_lpips,ssim,psnr_db,mask_fraction,output_sha256,provider_oracle,oracle_control
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage,"Vision en+ru; Paddle ch",0.338,0.107,0.832,24.4,,bed68cf79a023cad582a11b41c4c2962bd90a218037ce931a2b3a8697e3bf4bd,no_openai_signals_detected,source-positive control established earlier
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_02_23 PM.png",qwen-zimage-text-restored,"Vision en+ru; Paddle ch",0.007,0.174,0.703,13.9,0.154,3221b2694f39420d509dd37cdaf69581432f9a189e87397346de0c78762402c6,no_openai_signals_detected,qwen-zimage clean in same run
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage,"Vision en+ru; Paddle ch",0.305,0.095,0.818,26.0,,83e777ad28bf80a8e5aa7d2e6ef2348337c667212e2c71075460352ad7423713,no_openai_signals_detected,source-positive control established earlier
+2026-08-13,2d03a00,"ChatGPT Image May 31, 2026, 02_03_55 PM.png",qwen-zimage-text-restored,"Vision en+ru; Paddle ch",0.007,0.162,0.682,14.6,0.170,95812399424feb70fc56d3e518d1ae0a3a50a3c2049d5902b00686ede9d3022d,no_openai_signals_detected,qwen-zimage clean in same run
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage,"Paddle ch",0.074,0.289,0.696,23.3,,4b05489c7a79f993d35d32528de792baad5a3e33ee950afa147caf5d3e0621a6,synthid_detected,inconclusive account: clean baseline not reproduced
+2026-08-13,2d03a00,Gemini_Generated_Image_633uuy633uuy633u.png,qwen-zimage-text-restored,"Paddle ch",0.111,0.328,0.653,18.1,0.051,2ab7666a4161ff0e12c53d9412b23d013970c7613a8a55a1b1ae8a2744e3290d,synthid_detected,inconclusive account: qwen-zimage control also detected
@@ -0,0 +1,6 @@
+date,upstream_commit,workflow_sha256,fixture,route,seed,global_strength,face_strength,face_count,id_cos,face_lpips,lapvar_ratio,img_lpips,ssim,psnr_db,output_sha256,gemini_oracle,implementation
+2026-08-13,3007d0351596ae0a78b7074dae7ad179710b1e48,41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6,comparison/before/12.png,official-before,,,,10,,,,,,,056276464387c883f5fd89bd2f1415faeb425abb3153cd10efb25d1b9053bdc2,synthid_detected,upstream-published-bytes
+2026-08-13,3007d0351596ae0a78b7074dae7ad179710b1e48,41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6,comparison/after/12.png,official-after,,,,10,0.975,0.093,0.354,0.172,0.820,23.8,a822d805666224adb3e360b3ee89e14f20a341f1e7c4d3a5d3d5251e9286aefe,clean,upstream-published-bytes
+2026-08-13,3007d0351596ae0a78b7074dae7ad179710b1e48,41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6,Gemini_Generated_Image_y48j3cy48j3cy48j.png,visible-metadata-control,0,,,18,,,,,,,9f67264beb1da66a710885464a11f2ae85989094149f6145521d302cd2db940b,synthid_detected,project-runtime-control
+2026-08-13,3007d0351596ae0a78b7074dae7ad179710b1e48,41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6,Gemini_Generated_Image_y48j3cy48j3cy48j.png,upstream-global-close-reproduction,0,0.153999996,,18,0.589,0.217,0.331,0.286,0.647,24.3,281a1d28d786d157cbbd13d00a32b215de4dfb3174a3c5e4e83c843e5402138f,clean,diffsynth-bf16-yunet-sam
+2026-08-13,3007d0351596ae0a78b7074dae7ad179710b1e48,41911b3b8e01bf51450361dc8beccd84c9513f78ddb160430cdd9bebc294adf6,Gemini_Generated_Image_y48j3cy48j3cy48j.png,upstream-full-close-reproduction,0,0.153999996,0.13209025065104166,18,0.783,0.083,0.470,0.260,0.659,24.7,2d7a8f76f98b905f14d69e5f9e11979610c72c6be7b6ac298dec4659f17bd819,clean,diffsynth-bf16-yunet-sam
@@ -398,6 +398,37 @@ schedule, CFG 1.0 and CUDA, so every one of those flags existed only to be refus
 several layers down. They are not parsed at all now, which fails at the point the
 user can act on rather than after a model load.

+### Restore operator-verified text
+
+`--text-manifest` enables the experimental `vae-glyphs` post-pass. It reconstructs
+the source with the Qwen VAE, blends 15% of that reconstruction into the normal
+`qwen-zimage` result, erases the annotated candidate glyphs with LaMa, and composites
+only the reconstructed glyph cores through source-derived silhouettes. It does not
+run OCR or choose which strings are correct.
+
+Install the combined extra and run only with a manually reviewed manifest:
+
+```bash
+uv tool install --force "remove-ai-watermarks[text-restoration]"
+remove-ai-watermarks invisible image.png -o clean.png \
+  --pipeline qwen-zimage --text-manifest verified-lines.json --force
+```
+
+The manifest is a JSON object with `schema_version: 1`, `verified: true`, decoded
+RGB dimensions, `source_pixel_sha256`, and a non-empty `lines` array. Each line has
+an integer `[x1, y1, x2, y2]` box, exact `text`, a non-empty `script`, and an optional
+angle from -30 to 30 degrees. Lines must be in top-to-bottom, left-to-right order.
+The hash binds the annotations to decoded RGB geometry and pixels, so metadata-only
+container changes remain valid while a resized or edited source fails closed. The
+experimental helper
+`remove_ai_watermarks._internal.text_restoration.source_pixel_sha256` computes it.
+
+This mode is supported only by `qwen-zimage` at native untiled geometry with
+`humanize=0`, `unsharp=0`, and adaptive polish disabled. `all` also accepts the flag,
+but its manifest must match the pixels entering the invisible stage; if visible-mark
+removal changes those pixels, the hash check rejects the run. One oracle verdict does
+not certify another manifest, seed, model/runtime version, or output hash.
+
 ### Work with limited memory

 Lower CUDA memory pressure:
@@ -87,6 +87,15 @@ removal, metadata stripping and every `identify` command still run anywhere.
 Video SynthID regeneration is a separate VAE path and does still run on CPU or MPS;
 it needs the `diffusion` extra, not this one.

+The experimental verified-text post-pass additionally needs LaMa:
+
+```bash
+uv tool install --force "remove-ai-watermarks[text-restoration]"
+```
+
+That extra includes `qwen-zimage` and `lama`; it does not add OCR. Text strings and
+line boxes must be reviewed before the run.
+
 ## Feature extras

 Extras are composable. Install only the capabilities and file formats the
@@ -104,6 +113,7 @@ application actually uses:
 | `migan` | MI-GAN ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
 | `lama` | big-LaMa ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
 | `qwen-zimage` | Invisible image-watermark removal, both CUDA-only profiles | `diffusion`, DiffSynth | Yes |
+| `text-restoration` | Opt-in verified Qwen-VAE glyph restoration | `qwen-zimage`, `lama` | Yes |
 | `all` | Every production feature available on the active Python | All compatible rows above | Yes |
 | `dev` | Tests, linting, typing, and upstream parity checks | `video`, `detect`, upstream invisible-watermark | Yes, for parity tests |

@@ -118,6 +128,8 @@ flowchart LR
    migan --> visible
    lama --> visible
    qwen["qwen-zimage"] --> diffusion
+    text["text-restoration"] --> qwen
+    text --> lama
    heif
    trustmark
 ```
@@ -61,8 +61,25 @@ change even when the watermark is successfully disrupted.
 Both are CUDA only and differ only in the global regeneration model: each
 conditions that stage on a canny edge map, which preserves structure but not
 identity or exact texture, and each then runs the same face stage.
-`qwen-zimage` is the higher fidelity of the two. Both are large, slow, and may
-still alter small text or difficult faces.
+Existing face evaluations favor `qwen-zimage`, but there is no blanket fidelity
+ordering across content types. A fixed-seed, three-scene text comparison at the
+profile defaults found no stable winner: SDXL won one poster, Qwen won one, and
+the Chinese sign tied. Both are large, slow, and may still alter small text or
+difficult faces. The measurements and their OCR and oracle caveats are tracked
+in [`data/evaluations/fidelity/`](../data/evaluations/fidelity/README.md).
+A global Z-Image Turbo prototype preserved text substantially better at low
+strength, but it has no useful cross-provider operating point and is not a
+supported profile. Automatic text restorers also remain research-only:
+fresh-font and silhouette variants visibly changed typography. The higher-fidelity
+`vae-glyphs` route is available only as an experimental opt-in with verified strings
+and line geometry. It builds its donor internally but still requires an independently
+clean global anchor. Automatic OCR and line-box proposals are not reliable enough to
+remove those requirements, and exact oracle results do not establish a general mask,
+seed, runtime, or provider operating range. Qwen-Image-2.0 is hosted-only and exposes
+no equivalent low-strength denoise control. Exact experiments, controls, and pass
+rates are kept in
+[`text-protection-research.md`](text-protection-research.md) and the
+[`fidelity` evaluation record](../data/evaluations/fidelity/README.md).

 ### Removal cannot be verified locally for proprietary SynthID

@@ -175,6 +192,15 @@ certified at a fixed seed. The live resolver is
 | `qwen-zimage` | CUDA only, large model stack, and limited broad certification across seeds and content. |
 | `sdxl-zimage` | CUDA only. Its strength ladder is flat per vendor, not a resolution curve, because flat values are what was measured. |

+Only manually verified `vae-glyphs` is an optional production stage, and it is
+experimental rather than a default.
+OCR plus LaMa recovered literal poster text but changed fonts and worsened whole-image
+fidelity. Restricting it to OCR-mismatched lines improved the tradeoff but still
+left a local shadow on one poster. The published AnyText2 SD1.5 checkpoint
+substituted Chinese characters and increased CER on the sign fixture. AnyText2XL
+is not published, and the released wrapper truncates individual text lines after
+20 characters.
+
 The `controlnet`, `sdxl`, `qwen` and `default` profiles were removed, not aliased
 onward: a retired name is rejected at parse time rather than routed into a profile
 the caller never chose. There is no `--model`, `--steps`, `--guidance-scale`,
@@ -949,6 +949,57 @@ orchestration, YuNet integration, SAM selection, masks, sizing helpers, and pixe
 compositing are implemented for this runtime. Changing a calibrated model input
 requires the same provider-oracle and identity evaluation as a model change.

+#### Verified text restoration
+
+[`_internal/text_restoration.py`](../src/remove_ai_watermarks/_internal/text_restoration.py)
+implements the opt-in `vae-glyphs` stage. A versioned manifest carries manually
+reviewed strings and source-space line boxes, plus a SHA-256 over decoded RGB width,
+height, and pixels. Validation happens before model loading. The product never treats
+OCR confidence as verification.
+
+When enabled, `QwenZImagePipeline` reconstructs the source once through its already
+loaded Qwen VAE, runs the ordinary global and face stages, blends 15% of the VAE
+reconstruction into that clean result, and calls the shared restoration compositor.
+The compositor derives binary source and candidate silhouettes, groups nearby lines,
+uses LaMa for the initial and residual-glyph erase passes, paints fresh silhouette
+edges, then copies the Qwen-VAE core with a 0.5-pixel feather. The evaluation script
+imports these same mask and compositing helpers so the two implementations cannot
+silently drift.
+
+The stage is deliberately narrower than the engine: it rejects `sdxl-zimage`, tiles,
+resolution caps, humanize, unsharp, and adaptive polish. Those combinations change
+geometry or final pixels after the verified layer and have no measured oracle result.
+It remains opt-in because annotations are manual and provider verdicts apply only to
+the exact tested output hashes, not to the mechanism in general.
+
+A matched stage-isolation check on the 18-face Gemini portrait grid confirms the
+division of responsibility. The visible-cleaned, metadata-stripped control and the
+Z-Image face-only output were both SynthID-positive; Qwen global-only and the full
+Qwen-then-Z-Image output were both clean. The face stage raised identity cosine from
+0.589 to 0.852 and reduced face LPIPS from 0.217 to 0.050 without reintroducing a
+detectable whole-image signal. Thus Z-Image is a masked fidelity repair stage here,
+not the watermark-removal stage. Exact hashes, metrics, strengths, and the one-fixture,
+one-seed caveat are recorded in
+[`data/evaluations/fidelity/face-stage-isolation-2026-08-13.csv`](../data/evaluations/fidelity/face-stage-isolation-2026-08-13.csv).
+
+The public Synthid-Bypass v2 graph was subsequently audited at upstream commit
+`3007d035`. Its saved-output path confirms the same division: Qwen-Image-2512
+Lightning plus Canny is global, and Z-Image Turbo exists only inside the masked
+face detailer. The connected face path is YOLOv8-face plus SAM; the MediaPipe nodes
+described by the upstream README and the 1.2-megapixel normalization node do not
+reach `SaveImage`. Upstream also applies its adaptive face strength directly,
+whereas this implementation multiplies it by `FACE_DENOISE_SCALE = 0.5`.
+
+A close reproduction on the same portrait fixture kept the control positive and
+made both Qwen global-only and full Qwen-then-Z-Image outputs clean. Applying the
+upstream-strength face pass raised identity from 0.589 to 0.783 and reduced face
+LPIPS from 0.217 to 0.083, but remained worse than this profile's 0.852 and 0.050.
+The published upstream pair 12 was also independently checked positive before and
+clean after, with 0.975 identity. Exact workflow provenance, hashes, metrics,
+oracle outcomes, and the DiffSynth/GGUF, scheduler, detector, and seed caveats are
+recorded in
+[`data/evaluations/fidelity/upstream-v2-reproduction-2026-08-13.csv`](../data/evaluations/fidelity/upstream-v2-reproduction-2026-08-13.csv).
+
 ### SDXL plus Z-Image

 [`_internal/sdxl_zimage_pipeline.py`](../src/remove_ai_watermarks/_internal/sdxl_zimage_pipeline.py)
@@ -596,6 +596,23 @@ engine = InvisibleEngine(pipeline="sdxl-zimage")
 The `qwen-zimage` extra is required for both profiles: each runs the same
 DiffSynth Z-Image face stage.

+The opt-in verified-text stage uses the same `text_manifest` argument as the CLI:
+
+```python
+engine.remove_watermark(
+    Path("watermarked.png"),
+    Path("clean.png"),
+    text_manifest=Path("verified-lines.json"),
+)
+```
+
+Install `remove-ai-watermarks[text-restoration]`. The manifest schema and safety
+constraints are documented in the CLI guide. The engine verifies its decoded RGB
+hash before loading the diffusion models and rejects SDXL, tiling, downscaling, and
+postprocessing combinations that were not evaluated. `InvisibleOptions` exposes the
+same field for `remove_all`; after a visible-stage edit, the manifest must be built
+against the staged pixels rather than the pristine source.
+
 `remove_watermark` takes strength, seed, tiling, resolution, and postprocessing
 controls. It takes no model id, step count or guidance scale, and neither does the
 constructor: each profile pins its model stack, its per-stage schedule and CFG
@@ -143,3 +143,203 @@ Deep-research workflow run `wf_118b9a03-3eb` (2026-05-29). Findings adversariall
 (2/3 refutes required to kill a claim). This note records research only; no code change is
 implied until a prototype validates fidelity and the SynthID-scrub guarantee on the
 restored output.
+
+## 2026-08-13 OCR plus LaMa prototype
+
+A lightweight version of the recommended two-stage architecture was tested on
+the three tracked text fixtures. It used the already-clean Qwen result as the
+base, recognized the source text, removed source and Qwen glyph contours with
+LaMa, and rendered the recognized strings as fresh pixels. No source pixels
+were pasted back.
+
+The result validates only part of the hypothesis. Character-weighted CER on the
+two multilingual posters fell from 0.338/0.305 to 0.007/0.007, and OpenAI Verify
+remained clean for both matched Qwen and restored pairs. However, replacement
+fonts changed the design, whole-image LPIPS worsened by 0.067 on each poster,
+and the light poster retained a shadow in one small English line. On the Chinese
+sign, punctuation errors moved CER from 0.074 to 0.111. The Google verifier run
+was inconclusive because the available account detected both the restored image
+and the byte-identical Qwen control that a previous account had called clean.
+
+The experiment supports a proper glyph-conditioned restorer, not shipping the
+plain OCR/system-font compositor. Exact hashes, metrics, masks, and provider
+verdicts are tracked in
+[`data/evaluations/fidelity/text-restoration-2026-08-13.csv`](../data/evaluations/fidelity/text-restoration-2026-08-13.csv).
+
+## 2026-08-13 AnyText2 follow-up
+
+The official AnyText2 SD1.5 checkpoint was then tested as the glyph-conditioned
+restorer. Its ModelScope entry and code are Apache 2.0, and the downloaded
+checkpoint matched the published SHA-256. An official blackboard edit completed
+successfully before the fixture run, establishing that the runtime reproduced
+the model rather than silently exercising a fallback.
+
+On the Chinese sign, a three-line local edit over the exact Qwen output scored
+CER 0.185 under the standard detector. Font mimic from the source glyph masks
+scored 0.222. Whole-image LPIPS changed from 0.289 to 0.338 and 0.345 respectively.
+A padded crop-recognition check showed why the comparison matters: Qwen contained
+the full correct text and scored 0.000, while default AnyText2 still scored 0.074
+with two substituted characters and mimic remained at 0.222. The provider oracle
+was deliberately not called because these variants had already failed the
+content-fidelity gate.
+
+AnyText2 is therefore not the missing production restorer in its published form.
+The negative result is narrower than the model family: only the SD1.5 checkpoint
+is public, while AnyText2XL remains listed as future work. The current wrapper
+also truncates each quoted text line to 20 characters, which prevents a direct
+test of several tracked English and Russian poster lines. Exact model provenance,
+output hashes, and metrics are recorded in
+[`data/evaluations/fidelity/anytext2-restoration-2026-08-13.csv`](../data/evaluations/fidelity/anytext2-restoration-2026-08-13.csv).
+
+The same cross-check exposes a measurement bug in the earlier lightweight
+restoration result. Paddle detection made tight boxes that omitted the final
+Chinese full stop from two lines, producing Qwen CER 0.074 although the pixels
+and padded recognition were correct. Adding 16-32 pixels of horizontal context
+restored every punctuation mark. A deterministic rerender using the full OCR
+strings and the closest of nine mask-scored CJK fonts also reached detector CER
+0.000, but its heavier Hiragino Sans GB W6 glyphs raised LPIPS from 0.289 to 0.327.
+The actionable design is selective restoration: compare padded source and output
+recognition first, and preserve the Qwen output when they already match.
+
+That policy was tested on the two multilingual posters. A manual prototype kept
+the lines whose padded source and Qwen recognition agreed and rerendered only the
+changed lines. The edited mask fell from 15.4%/17.0% in the full compositor to
+5.5%/5.5%. On a single Paddle `en+ru+ch` route, CER changed from 0.378/0.413 for
+Qwen to 0.101/0.112, while LPIPS was only 0.120/0.103 rather than the full
+compositor's 0.174/0.162. The different OCR route is intentional and all three
+variants were remeasured through it; these CER values are not directly
+comparable with the earlier Vision/Paddle hybrid results.
+
+Both selective outputs returned `No OpenAI signals detected` in OpenAI Verify,
+and the original light poster returned `Generated with OpenAI tools` immediately
+afterward as the positive control. The dark poster was visually clean. The light
+poster still showed one local old-text shadow, so mask construction remains the
+blocking defect. Exact hashes, metrics, mask fractions, and oracle controls are
+in
+[`data/evaluations/fidelity/selective-text-restoration-2026-08-13.csv`](../data/evaluations/fidelity/selective-text-restoration-2026-08-13.csv).
+
+Uniformly expanding every selected glyph mask by two pixels removed that light
+poster shadow and moved CER from 0.101 to 0.081 without a meaningful LPIPS cost.
+It also expanded the edited area from 5.5% to 7.0%, and the identical rule made
+the dark poster worse at CER 0.147 rather than 0.112. Those follow-up bytes have
+not been oracle-checked. The next prototype should detect residual source glyphs
+per line and expand only the failing component, rather than introducing another
+poster-wide tuning constant.
+
+The manual selection was then replaced with per-line padded recognition. A line
+is left untouched only when the source recognizer is sufficiently consistent
+with the verified line and normalized source/candidate recognition agree
+exactly. The automatic rule reproduced the manual selection on the dark poster
+and correctly kept one additional large Russian line on the light poster. It
+reduced that poster's effective edited area from 5.5% to 4.1%.
+
+Mask cleanup became a measured second pass rather than a global dilation. After
+the first LaMa erase, the prototype finds contrast that remains specifically at
+the original glyph positions, dilates only that residual, and erases it before
+fresh text is drawn. This removed the visible double glyphs from both posters.
+The automatic outputs scored LPIPS 0.113/0.104 and detector CER 0.123/0.119, with
+effective edited fractions of 4.1%/5.7%. The light aggregate CER is pessimistic:
+the page detector fragments its untouched Cyrillic line into Latin-like pieces,
+while the padded Russian recognizer returns the exact expected text.
+
+OpenAI Verify returned `No OpenAI signals detected` for both new hashes and then
+`Generated with OpenAI tools` for the original light poster in the same browser
+sequence. Reproducing the prototype from the tracked script exposed harmless
+ONNX rounding on the light image: 386 pixels differed by at most one channel
+value from the initially checked hash. The exact tracked output hash
+`900def5a...` was therefore submitted separately, returned clean, and was
+followed immediately by another positive source result. The dark tracked output
+was byte-identical to the initially checked hash.
+
+The Chinese sign provides a no-edit control for the selector. Detector boxes for
+its three large lines overlap vertically, so Latin-style vertical padding
+incorrectly mixed neighboring lines. Horizontal-only CJK padding captured the
+terminal punctuation without mixing neighbors and made source and candidate
+recognition agree exactly on all three lines.
+The script then produced a zero mask and copied the Qwen candidate byte for byte.
+This prevents the heavier-font regression seen in the earlier CJK compositor,
+but does not resolve the unavailable Google-negative oracle control.
+
+The result clears the measured OpenAI gate without a manual line selection, but
+is not a production design yet. It still assumes verified source strings and
+line boxes, relies on macOS system fonts, and needs evaluation on uncurated
+layouts, rotated text, false OCR matches, automatic line-box discovery, and a
+reproducible Google-negative control.
+
+The later source-silhouette experiment removed the font lookup but did not meet
+the actual visual requirement. Native-size review found changed stroke weight,
+color variation, edge antialiasing, and decorative details even where crop OCR
+was exact. A narrower `vae-glyphs` experiment uses the silhouette only as a
+support mask: exact pixels come from a Qwen-VAE reconstruction, while a fresh
+flat-color silhouette supplies only the outer edge beneath that core. Across 48
+typography cases (548 annotated lines), this improved glyph-edge MAE in 48/48
+and text-box SSIM in 47/48. The median values moved from 37.59 to 32.42 and from
+0.854 to 0.914 respectively. One separate untracked core-only output with a
+1.43% donor area returned `No OpenAI signals detected`; the tracked script then
+reproduced those exact bytes. A 0.5-pixel feathered sibling with 2.75% nonzero
+donor area improved mean text-box SSIM from 0.957 to 0.960, with a 0.918 minimum
+across 15 verified lines, while full-image RGB SSIM reached 0.864. Crop OCR
+recovered from 7/15 exact lines on the raw pass to 14/15, equal to the source's
+own recognizer floor. Whole-image LPIPS was 0.082, but only 0.108% of pixels
+remained exactly equal to the source and the detected face's Laplacian-variance
+ratio was 0.670, confirming residual global smoothing. A same-session
+OpenAI Verify sequence detected the exact source control in 1/1 check, returned
+clean for the raw global pass and clean-fidelity anchor in 1/1 each, and returned
+clean for the feathered output in 3/3 repeats. The tracked script reproduced
+that feathered file byte for byte. This proves two materially better exact-output
+Pareto points, not a general operating range. The 48 matrix outputs and other
+mask sizes remain oracle-unverified.
+
+The first Google oracle sample did not transfer. For the synthetic CJK sign,
+two separate work accounts detected SynthID in both the resaved positive source
+control and the exact Qwen-VAE donor candidate. The candidate's text-box SSIM
+improved from 0.791 to 0.835 and its glyph-edge MAE from 35.48 to 22.49, but its
+3.67% donor layer did not create a clean operating point. The Qwen silhouette
+base was already detected, as was the earlier original-sign Qwen baseline in the
+same account. The measured cause is therefore insufficient global Google
+regeneration, not a demonstrated watermark regression from the text donor.
+Google needs a stronger oracle-clean global anchor before the narrow donor can
+be evaluated there.
+
+A targeted follow-up supplied that missing anchor for one CJK case. An explicit
+Qwen global pass at strength 0.30 returned no reliable SynthID signal in Gemini,
+while the matched source control was detected. Applying the 0.5-pixel
+`vae-glyphs` layer to that clean anchor changed 1.17% of the image and raised
+mean text-box SSIM from 0.765 to 0.964 (minimum 0.963), with mean text-box MAE
+falling from 20.74 to 5.38. The post-donor output then returned clean in 3/3
+Gemini checks across two work accounts. A separate 18-face fixture with four
+tiny UI-text lines also had a clean strength-0.30 anchor; its donor layer changed
+0.70% of the image, raised mean text-box SSIM from 0.617 to 0.976 (minimum
+0.972), and returned clean in 3/3 checks across the same two accounts. One
+quota-exceeded response is excluded from both pass rates. These are two exact
+oracle-certified outputs, not a general operating range: other layouts, masks,
+strengths, seeds, and donor areas remain unverified.
+
+An automatic-box follow-up merged Paddle word detections by vertical overlap.
+It found exactly 20/20 poster lines and 3/3 sign lines, with mean IoU 0.857,
+0.847, and 1.000 against the verified boxes. That structural match was not
+sufficient: reusing the annotation crop padding changed recognition decisions,
+reduced preserved dark-poster lines from 7 to 4, and expanded its edited
+fraction from 5.7% to 11.2%.
+
+A recognition-only sweep isolated the crop sensitivity. Limiting vertical
+padding around detector boxes to 8-12% of line height reproduced the verified-
+box decision vectors on both posters; 10% was used for a full follow-up. It kept
+8/20 and 7/20 lines with edited fractions of 4.1% and 5.7%, and visual inspection
+found no double glyphs. Whole-image LPIPS remained 0.113/0.104, but detector CER
+was 0.127/0.154 instead of 0.123/0.119. The dark regression failed the fidelity
+gate, so these hashes were not submitted to the provider oracle. The opt-in flag
+remains only to reproduce the negative result. Count, IoU, and matching decision
+vectors are therefore insufficient gates for automatic boxes; the next design
+needs box rectification or recognition stability under crop jitter before it
+can replace verified geometry.
+
+Removing verified strings was tested separately with an annotation-seed dry
+run. It detects boxes, chooses `en`, `ru`, or `ch` from Unicode script, and
+accepts a draft only when three crop paddings normalize identically and every
+confidence is at least 0.85. One execution proposed 20 and 18 poster lines, but
+exact-text precision against the held-out annotations was only 90.0% and 94.4%.
+The stable errors were punctuation: one lost English comma and an ideographic
+comma consistently replaced by ASCII. Thus crop agreement and confidence do
+not verify literal text. The helper is retained only to seed annotations; every
+proposal needs manual review before it can enter the restoration compositor.
@@ -120,6 +120,11 @@ qwen-zimage = [
    "diffsynth>=2.0.17,<3",
    "torchvision>=0.20.0",
 ]
+# Opt-in verified-text reconstruction over qwen-zimage. LaMa removes the changed
+# candidate glyphs before exact Qwen-VAE cores are composited back.
+text-restoration = [
+    "remove-ai-watermarks[qwen-zimage,lama]",
+]
 # Adobe TrustMark decoder -- the open, keyless watermark behind Adobe Durable
 # Content Credentials (soft-binding alg ``com.adobe.trustmark.P``). Optional
 # because it pulls torch and downloads model weights on first use. identify()
@@ -161,7 +166,7 @@ dev = [
 ]
 # ``qwen-zimage`` already pulls ``diffusion``; naming both would suggest diffusion is
 # independently sufficient for a removal, which it is not.
-all = ["remove-ai-watermarks[video,heif,detect,trustmark,qwen-zimage,lama,migan]"]
+all = ["remove-ai-watermarks[video,heif,detect,trustmark,text-restoration,migan]"]

 [project.scripts]
 remove-ai-watermarks = "remove_ai_watermarks.cli:main"
@@ -0,0 +1,34 @@
+"""Pure text-normalization helpers shared by evaluation scripts."""
+
+from __future__ import annotations
+
+import unicodedata
+
+
+def normalize_text(text: str) -> str:
+    """Normalize text for layout-independent evaluation comparisons."""
+    return "".join(unicodedata.normalize("NFC", text).casefold().split())
+
+
+def levenshtein_normalized(left: str, right: str) -> float:
+    """Return normalized Levenshtein distance without changing either input."""
+    if not left and not right:
+        return 0.0
+    previous = list(range(len(right) + 1))
+    for left_index, left_character in enumerate(left, start=1):
+        current = [left_index]
+        for right_index, right_character in enumerate(right, start=1):
+            current.append(
+                min(
+                    current[-1] + 1,
+                    previous[right_index] + 1,
+                    previous[right_index - 1] + (left_character != right_character),
+                )
+            )
+        previous = current
+    return previous[-1] / max(len(left), len(right))
+
+
+def normalized_edit_distance(left: str, right: str) -> float:
+    """Normalize text, then return Levenshtein distance over the result."""
+    return levenshtein_normalized(normalize_text(left), normalize_text(right))
@@ -3,10 +3,9 @@
 # dependencies = [
 #   "click",
 #   "numpy",
-#   "opencv-python-headless",
+#   "opencv-python-headless<5",
 #   "pillow",
 #   "scikit-image",
-#   "rapidfuzz",
 #   "torch",
 #   "lpips",
 #   "paddleocr",
@@ -29,7 +28,7 @@ metrics run only where faces are detected, text metrics only where text is.

 Two subcommands:

-  ocr      -- OCR images (PaddleOCR PP-OCRv6) into a JSON {basename: text} file.
+  ocr      -- OCR images (PaddleOCR defaults) into a JSON {basename: text} file.
              Run this on the ORIGINALS, hand-verify/correct the file, and it
              becomes the ground truth for ``compare --ground-truth`` -- the clean
              way to score text, since OCR-vs-OCR is doubly noisy (errors on both
@@ -54,6 +53,7 @@ Usage:
 from __future__ import annotations

 import json
+import sys
 import unicodedata
 from dataclasses import dataclass, field
 from pathlib import Path
@@ -65,7 +65,10 @@ import numpy as np
 from _plain_console import Console, Table

 console = Console()
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))

+from scripts._text_eval import levenshtein_normalized  # noqa: E402

 # ── helpers ──────────────────────────────────────────────────────────

@@ -89,7 +92,7 @@ def _norm(text: str) -> str:
    return "".join(unicodedata.normalize("NFC", text).split())


-# ── text: PaddleOCR (PP-OCRv6) ───────────────────────────────────────
+# ── text: PaddleOCR defaults ─────────────────────────────────────────

 # Our lang codes -> PaddleOCR lang. The 'ch' model also reads Latin; 'ru' reads
 # Cyrillic + Latin. Multiple langs in one image -> run each model, union detections.
@@ -165,9 +168,7 @@ def _ocr_lines(bgr: np.ndarray, langs: list[str], min_score: float = 0.5) -> lis


 def _cer(ref: str, hyp: str) -> float:
-    from rapidfuzz.distance import Levenshtein
-
-    return Levenshtein.normalized_distance(_norm(ref), _norm(hyp))
+    return levenshtein_normalized(_norm(ref), _norm(hyp))


 # ── face: detection + ArcFace + texture ──────────────────────────────
@@ -0,0 +1,128 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "click",
+#   "huggingface-hub>=0.20.0",
+#   "numpy",
+#   "onnxruntime>=1.24.0",
+#   "opencv-python-headless<5",
+#   "paddleocr>=3.3.3",
+#   "paddlepaddle",
+#   "pillow",
+# ]
+# ///
+"""Infer stable source-text lines without modifying an image.
+
+This evaluation-only dry run proposes line annotations for selective text
+restoration. Every proposal still needs human verification: stable OCR can lose
+punctuation with high confidence. It separately flags lines whose recognition
+changes under crop jitter or whose minimum confidence is below the threshold.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import json
+import logging
+import os
+import sys
+import unicodedata
+from pathlib import Path
+from typing import Any
+
+import click
+import numpy as np
+from PIL import Image
+
+log = logging.getLogger(__name__)
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+RESTORATION_SCRIPT = ROOT / "scripts/selective_text_restoration.py"
+
+from scripts._text_eval import normalize_text  # noqa: E402
+
+
+def _load_restoration_module() -> Any:
+    spec = importlib.util.spec_from_file_location("selective_text_restoration_for_inference", RESTORATION_SCRIPT)
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"unable to load {RESTORATION_SCRIPT}")
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def _has_script(text: str, script: str) -> bool:
+    return any(script in unicodedata.name(character, "") for character in text)
+
+
+def choose_language(probes: dict[str, tuple[str, float]]) -> str:
+    if _has_script(probes["ch"][0], "CJK"):
+        return "ch"
+    if _has_script(probes["ru"][0], "CYRILLIC"):
+        return "ru"
+    return "en"
+
+
+def stable_recognition(reads: list[tuple[str, float]], min_score: float = 0.85) -> str | None:
+    normalized = {normalize_text(text) for text, _score in reads}
+    if len(normalized) != 1 or min(score for _text, score in reads) < min_score:
+        return None
+    return reads[0][0]
+
+
+@click.command()
+@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
+@click.option("--out", required=True, type=click.Path(dir_okay=False, path_type=Path))
+@click.option("--min-score", default=0.85, show_default=True, type=click.FloatRange(0.0, 1.0))
+def main(source: Path, out: Path, min_score: float) -> None:
+    """Write draft line text for SOURCE; manually verify every proposal."""
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True"
+    from paddleocr import PaddleOCR, TextRecognition
+
+    restoration = _load_restoration_module()
+    source_rgb = np.asarray(Image.open(source).convert("RGB"))
+    detector = PaddleOCR(
+        lang="ch",
+        use_doc_orientation_classify=False,
+        use_doc_unwarping=False,
+        use_textline_orientation=False,
+    )
+    engines = {
+        "en": TextRecognition(model_name="en_PP-OCRv5_mobile_rec"),
+        "ru": TextRecognition(model_name="eslav_PP-OCRv5_mobile_rec"),
+        "ch": TextRecognition(model_name="PP-OCRv5_server_rec"),
+    }
+    boxes = restoration.detect_line_boxes(detector, source_rgb)
+    accepted = []
+    rejected = []
+    for box in boxes:
+        probes = {}
+        for language, engine in engines.items():
+            script = "cjk" if language == "ch" else "alphabetic"
+            line = restoration.TextLine(box, "", script)
+            probes[language] = restoration._recognize(engine, source_rgb, line, 0.1)
+        language = choose_language(probes)
+        script = "cjk" if language == "ch" else "alphabetic"
+        line = restoration.TextLine(box, "", script)
+        reads = [restoration._recognize(engines[language], source_rgb, line, ratio) for ratio in (0.08, 0.12, 0.2)]
+        text = stable_recognition(reads, min_score)
+        result = {
+            "box": box,
+            "script": script,
+            "language": language,
+            "reads": [{"text": value, "score": score} for value, score in reads],
+        }
+        if text is None:
+            rejected.append(result)
+        else:
+            accepted.append({"box": box, "text": text, "script": script, "min_score": min(score for _, score in reads)})
+    payload = {"source": source.name, "accepted": accepted, "rejected": rejected}
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+    log.info("Accepted %s lines and rejected %s uncertain lines", len(accepted), len(rejected))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,528 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "click",
+#   "huggingface-hub>=0.20.0",
+#   "numpy",
+#   "onnxruntime>=1.24.0",
+#   "opencv-python-headless<5",
+#   "paddleocr>=3.3.3",
+#   "paddlepaddle",
+#   "pillow",
+# ]
+# ///
+"""Evaluation-only text restoration over a scrubbed image.
+
+``vae-glyphs`` composites only thresholded glyph-core pixels from a separately
+generated VAE reconstruction over a fresh silhouette edge. ``source-glyphs``
+can preserve typeface, layout, color, and antialiasing when
+SOURCE is itself a regenerated layer. It must not be treated as safe when SOURCE
+is the watermarked original: a provider oracle detected SynthID after that exact
+paste-back experiment. ``source-silhouette`` instead transfers only a
+thresholded glyph shape, then synthesizes fresh flat-color pixels and
+antialiasing. ``rerender`` retains the system-font negative control. This is
+not a production stage and does not add PaddleOCR to the package graph.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import math
+import os
+import shutil
+import sys
+import unicodedata
+from dataclasses import asdict
+from pathlib import Path
+from typing import Any
+
+import click
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+log = logging.getLogger(__name__)
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+sys.path.insert(0, str(ROOT / "src"))
+
+from remove_ai_watermarks import region_eraser  # noqa: E402
+from remove_ai_watermarks._internal.text_restoration import VerifiedTextLine as TextLine  # noqa: E402
+from remove_ai_watermarks._internal.text_restoration import (  # noqa: E402
+    composite_fresh_text_edges,
+    composite_reconstructed_glyphs,
+    group_text_lines,
+    residual_glyph_mask,
+    source_silhouette_mask,
+)
+from scripts._text_eval import normalize_text, normalized_edit_distance  # noqa: E402
+
+if ROOT not in Path(region_eraser.__file__).resolve().parents:
+    raise RuntimeError("selective_text_restoration imported outside the current worktree")
+
+REGULAR_FONT = Path("/System/Library/Fonts/Supplemental/Arial.ttf")
+BOLD_FONT = Path("/System/Library/Fonts/Supplemental/Arial Bold.ttf")
+CJK_FONT = Path("/System/Library/Fonts/STHeiti Medium.ttc")
+
+
+def should_preserve_line(
+    expected: str,
+    source_text: str,
+    source_score: float,
+    candidate_text: str,
+    candidate_score: float,
+) -> bool:
+    if min(source_score, candidate_score) < 0.75:
+        return False
+    if normalized_edit_distance(expected, source_text) > 0.25:
+        return False
+    return normalize_text(source_text) == normalize_text(candidate_text)
+
+
+def composite_source_glyphs(
+    source_rgb: np.ndarray,
+    background_rgb: np.ndarray,
+    glyph_mask: np.ndarray,
+    *,
+    feather: float = 0.7,
+) -> np.ndarray:
+    """Composite exact source pixels inside a glyph mask with an outer feather."""
+    if source_rgb.shape != background_rgb.shape or source_rgb.shape[:2] != glyph_mask.shape:
+        raise ValueError("source, background, and glyph mask dimensions must match")
+    blurred = cv2.GaussianBlur(glyph_mask, (0, 0), feather) if feather > 0 else glyph_mask
+    alpha = np.maximum(glyph_mask, blurred).astype(np.float32) / 255.0
+    alpha = alpha[..., None]
+    combined = source_rgb.astype(np.float32) * alpha + background_rgb.astype(np.float32) * (1.0 - alpha)
+    return np.clip(combined, 0, 255).astype(np.uint8)
+
+
+def source_box_mask(
+    shape: tuple[int, int],
+    boxes: list[tuple[int, int, int, int]],
+) -> np.ndarray:
+    """Build a padded text-line mask for aligned regenerated layer compositing."""
+    height, width = shape
+    mask = np.zeros((height, width), dtype=np.uint8)
+    for x1, y1, x2, y2 in boxes:
+        pad = max(8, (y2 - y1) // 4)
+        x1, y1, x2, y2 = _clip_box((x1, y1, x2, y2), width, height, pad=pad)
+        mask[y1:y2, x1:x2] = 255
+    return mask
+
+
+def _clip_box(box: tuple[int, int, int, int], width: int, height: int, pad: int = 0) -> tuple[int, int, int, int]:
+    x1, y1, x2, y2 = box
+    return max(0, x1 - pad), max(0, y1 - pad), min(width, x2 + pad), min(height, y2 + pad)
+
+
+def foreground_mask(source_rgb: np.ndarray, box: tuple[int, int, int, int]) -> np.ndarray:
+    height, width = source_rgb.shape[:2]
+    line_height = box[3] - box[1]
+    x1, y1, x2, y2 = _clip_box(box, width, height, pad=max(6, int(line_height * 0.12)))
+    gray = cv2.cvtColor(source_rgb[y1:y2, x1:x2], cv2.COLOR_RGB2GRAY)
+    ring_pad = max(8, min(24, (y2 - y1) // 5))
+    rx1, ry1, rx2, ry2 = _clip_box((x1, y1, x2, y2), width, height, pad=ring_pad)
+    context = cv2.cvtColor(source_rgb[ry1:ry2, rx1:rx2], cv2.COLOR_RGB2GRAY)
+    ring = np.ones(context.shape, dtype=bool)
+    ring[y1 - ry1 : y2 - ry1, x1 - rx1 : x2 - rx1] = False
+    background_luma = float(np.median(context[ring])) if ring.any() else float(np.median(gray))
+    low, high = float(np.percentile(gray, 4)), float(np.percentile(gray, 96))
+    dark_contrast, light_contrast = background_luma - low, high - background_luma
+    contrast = max(light_contrast, dark_contrast)
+    threshold = max(24.0, min(72.0, contrast * 0.32))
+    if light_contrast > dark_contrast:
+        mask = (gray.astype(np.float32) >= background_luma + threshold).astype(np.uint8) * 255
+    else:
+        mask = (gray.astype(np.float32) <= background_luma - threshold).astype(np.uint8) * 255
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((2, 2), np.uint8))
+    dilation = 5 if line_height >= 48 else 3
+    mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * dilation + 1,) * 2))
+    result = np.zeros((height, width), dtype=np.uint8)
+    result[y1:y2, x1:x2] = mask
+    return result
+
+
+def _line_language(line: TextLine) -> str:
+    if line.script == "cjk":
+        return "ch"
+    return "ru" if any("CYRILLIC" in unicodedata.name(character, "") for character in line.text) else "en"
+
+
+def _recognition_box(
+    line: TextLine,
+    width: int,
+    height: int,
+    vertical_pad_ratio: float | None = None,
+) -> tuple[int, int, int, int]:
+    x1, y1, x2, y2 = line.box
+    line_height = y2 - y1
+    if line.script == "cjk":
+        left_pad = max(16, round(line_height * 0.2))
+        right_pad = max(16, round(line_height * 0.6))
+        return max(0, x1 - left_pad), y1, min(width, x2 + right_pad), y2
+    pad_x = max(16, line_height)
+    pad_y = max(8, line_height // 3) if vertical_pad_ratio is None else max(8, round(line_height * vertical_pad_ratio))
+    return max(0, x1 - pad_x), max(0, y1 - pad_y), min(width, x2 + pad_x), min(height, y2 + pad_y)
+
+
+def _recognize(
+    engine: Any,
+    image: np.ndarray,
+    line: TextLine,
+    vertical_pad_ratio: float | None = None,
+) -> tuple[str, float]:
+    height, width = image.shape[:2]
+    x1, y1, x2, y2 = _recognition_box(line, width, height, vertical_pad_ratio)
+    crop = image[y1:y2, x1:x2]
+    if crop.shape[0] < 64:
+        scale = 64 / crop.shape[0]
+        crop = cv2.resize(crop, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
+    result = next(iter(engine.predict(crop)))
+    return str(result.get("rec_text", "")), float(result.get("rec_score", 0.0))
+
+
+def _sample_text_color(
+    source_rgb: np.ndarray,
+    mask: np.ndarray,
+    box: tuple[int, int, int, int],
+) -> tuple[int, int, int]:
+    height, width = source_rgb.shape[:2]
+    x1, y1, x2, y2 = _clip_box(box, width, height, pad=2)
+    crop = source_rgb[y1:y2, x1:x2]
+    active = mask[y1:y2, x1:x2] > 0
+    pixels = crop[active]
+    luma = pixels.mean(axis=1)
+    background_luma = float(crop[[0, -1], :, :].reshape(-1, 3).mean(axis=1).mean())
+    if background_luma >= 128:
+        selected = pixels[luma <= np.percentile(luma, 20)]
+    else:
+        selected = pixels[luma >= np.percentile(luma, 80)]
+    return tuple(int(value) for value in np.median(selected, axis=0))
+
+
+def _render_line(image: Image.Image, line: TextLine, color: tuple[int, int, int]) -> None:
+    font_path = CJK_FONT if line.script == "cjk" else (BOLD_FONT if line.box[3] - line.box[1] >= 55 else REGULAR_FONT)
+    target_width, target_height = line.box[2] - line.box[0], line.box[3] - line.box[1]
+    draw = ImageDraw.Draw(image)
+    low, high = 4, max(8, target_height * 2)
+    font = ImageFont.truetype(str(font_path), low)
+    while low <= high:
+        size = (low + high) // 2
+        candidate = ImageFont.truetype(str(font_path), size)
+        bounds = draw.textbbox((0, 0), line.text, font=candidate)
+        if bounds[2] - bounds[0] <= target_width * 1.03 and bounds[3] - bounds[1] <= target_height * 1.08:
+            font, low = candidate, size + 1
+        else:
+            high = size - 1
+    bounds = draw.textbbox((0, 0), line.text, font=font)
+    y = line.box[1] + math.floor((target_height - (bounds[3] - bounds[1])) / 2) - bounds[1]
+    draw.text((line.box[0], y), line.text, fill=color, font=font)
+
+
+def _write_manifest(path: Path | None, payload: dict[str, Any]) -> None:
+    if path is None:
+        return
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def _vertical_overlap_ratio(left: tuple[int, int, int, int], right: tuple[int, int, int, int]) -> float:
+    overlap = max(0, min(left[3], right[3]) - max(left[1], right[1]))
+    return overlap / max(1, min(left[3] - left[1], right[3] - right[1]))
+
+
+def group_word_boxes(boxes: list[tuple[int, int, int, int]]) -> list[tuple[int, int, int, int]]:
+    groups: list[tuple[int, int, int, int]] = []
+    for box in sorted(boxes, key=lambda item: ((item[1] + item[3]) / 2, item[0])):
+        matches = []
+        for index, group in enumerate(groups):
+            if _vertical_overlap_ratio(box, group) < 0.45:
+                continue
+            horizontal_gap = max(0, max(box[0], group[0]) - min(box[2], group[2]))
+            line_height = min(box[3] - box[1], group[3] - group[1])
+            if horizontal_gap <= max(24, line_height * 3):
+                matches.append(index)
+        if not matches:
+            groups.append(box)
+            continue
+        index = max(matches, key=lambda item: _vertical_overlap_ratio(box, groups[item]))
+        x1, y1, x2, y2 = groups[index]
+        groups[index] = min(x1, box[0]), min(y1, box[1]), max(x2, box[2]), max(y2, box[3])
+    return sorted(groups, key=lambda item: ((item[1] + item[3]) / 2, item[0]))
+
+
+def detect_line_boxes(
+    engine: Any,
+    source_rgb: np.ndarray,
+    expected_count: int | None = None,
+) -> list[tuple[int, int, int, int]]:
+    boxes: list[tuple[int, int, int, int]] = []
+    for page in engine.predict(source_rgb):
+        detected = page.get("rec_boxes", None)
+        if detected is None or len(detected) == 0:
+            detected = page.get("rec_polys", [])
+        for score, raw_box in zip(page.get("rec_scores", []), detected, strict=False):
+            if float(score) < 0.5:
+                continue
+            points = np.asarray(raw_box, dtype=np.float32).reshape(-1)
+            if points.size == 4:
+                x1, y1, x2, y2 = points
+            else:
+                points = points.reshape(-1, 2)
+                x1, y1 = points.min(axis=0)
+                x2, y2 = points.max(axis=0)
+            boxes.append((round(float(x1)), round(float(y1)), round(float(x2)), round(float(y2))))
+    lines = group_word_boxes(boxes)
+    if expected_count is not None and len(lines) != expected_count:
+        raise click.ClickException(f"detected {len(lines)} source lines; expected exactly {expected_count}")
+    return lines
+
+
+def _load_lines(path: Path, key: str) -> list[TextLine]:
+    payload = json.loads(path.read_text(encoding="utf-8"))
+    return [
+        TextLine(tuple(item["box"]), item["text"], item["script"], float(item.get("angle", 0.0)))
+        for item in payload[key]
+    ]
+
+
+@click.command()
+@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
+@click.argument("candidate", type=click.Path(exists=True, dir_okay=False, path_type=Path))
+@click.option("--output", required=True, type=click.Path(dir_okay=False, path_type=Path))
+@click.option(
+    "--lines-json",
+    default=ROOT / "data/evaluations/fidelity/text-lines.json",
+    type=click.Path(exists=True, dir_okay=False, path_type=Path),
+)
+@click.option("--source-key", help="Key in lines JSON; defaults to source basename.")
+@click.option(
+    "--detect-boxes",
+    is_flag=True,
+    help="Detect source boxes; fail unless their count matches verified lines.",
+)
+@click.option(
+    "--restoration",
+    type=click.Choice(("vae-glyphs", "source-glyphs", "source-silhouette", "rerender")),
+    required=True,
+    help="Choose VAE glyph cores, regenerated pixels, fresh source shapes, or the system-font control.",
+)
+@click.option(
+    "--glyph-donor",
+    type=click.Path(exists=True, dir_okay=False, path_type=Path),
+    help="VAE reconstruction used only by --restoration vae-glyphs.",
+)
+@click.option(
+    "--glyph-feather",
+    type=click.FloatRange(min=0.0),
+    default=0.5,
+    show_default=True,
+    help="Outer donor-edge feather used only by --restoration vae-glyphs.",
+)
+@click.option(
+    "--selection",
+    type=click.Choice(("all", "changed")),
+    default="all",
+    show_default=True,
+    help="Restore every verified line or only OCR-confirmed changes.",
+)
+@click.option(
+    "--erase-background/--keep-background",
+    default=True,
+    show_default=True,
+    help="Erase candidate glyphs before compositing, or directly blend an aligned regenerated glyph layer.",
+)
+@click.option(
+    "--composite-mask",
+    type=click.Choice(("glyphs", "boxes")),
+    default="glyphs",
+    show_default=True,
+    help="Composite isolated glyphs or complete aligned text-line boxes.",
+)
+@click.option("--manifest", type=click.Path(dir_okay=False, path_type=Path))
+def main(
+    source: Path,
+    candidate: Path,
+    output: Path,
+    lines_json: Path,
+    source_key: str | None,
+    detect_boxes: bool,
+    restoration: str,
+    glyph_donor: Path | None,
+    glyph_feather: float,
+    selection: str,
+    erase_background: bool,
+    composite_mask: str,
+    manifest: Path | None,
+) -> None:
+    """Restore SOURCE text over the scrubbed CANDIDATE."""
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
+    if restoration == "rerender":
+        for font in (REGULAR_FONT, BOLD_FONT, CJK_FONT):
+            if not font.exists():
+                raise click.ClickException(f"required evaluation font is unavailable: {font}")
+    if restoration == "vae-glyphs" and glyph_donor is None:
+        raise click.ClickException("--glyph-donor is required for --restoration vae-glyphs")
+    if restoration != "vae-glyphs" and glyph_donor is not None:
+        raise click.ClickException("--glyph-donor is only valid with --restoration vae-glyphs")
+    os.environ["PADDLE_PDX_DISABLE_MODEL_SOURCE_CHECK"] = "True"
+    source_rgb = np.asarray(Image.open(source).convert("RGB"))
+    candidate_rgb = np.asarray(Image.open(candidate).convert("RGB"))
+    if candidate_rgb.shape != source_rgb.shape:
+        raise click.ClickException("source and candidate dimensions must match")
+    donor_rgb = np.asarray(Image.open(glyph_donor).convert("RGB")) if glyph_donor else None
+    if donor_rgb is not None and donor_rgb.shape != source_rgb.shape:
+        raise click.ClickException("source and glyph donor dimensions must match")
+    lines = _load_lines(lines_json, source_key or source.name)
+    annotation_boxes = [line.box for line in lines]
+    if detect_boxes:
+        from paddleocr import PaddleOCR
+
+        page_engine = PaddleOCR(
+            lang="ch",
+            use_doc_orientation_classify=False,
+            use_doc_unwarping=False,
+            use_textline_orientation=False,
+        )
+        boxes = detect_line_boxes(page_engine, source_rgb, len(lines))
+        lines = [TextLine(box, line.text, line.script) for line, box in zip(lines, boxes, strict=True)]
+    vertical_pad_ratio = 0.1 if detect_boxes else None
+    decisions = []
+    selected = list(lines)
+    if selection == "all":
+        decisions = [{"line": asdict(line), "selected": True, "reason": "all-lines"} for line in lines]
+    else:
+        from paddleocr import TextRecognition
+
+        engines = {
+            "en": TextRecognition(model_name="en_PP-OCRv5_mobile_rec"),
+            "ru": TextRecognition(model_name="eslav_PP-OCRv5_mobile_rec"),
+            "ch": TextRecognition(model_name="PP-OCRv5_server_rec"),
+        }
+        selected = []
+        for line in lines:
+            language = _line_language(line)
+            source_text, source_score = _recognize(engines[language], source_rgb, line, vertical_pad_ratio)
+            candidate_text, candidate_score = _recognize(engines[language], candidate_rgb, line, vertical_pad_ratio)
+            preserve = should_preserve_line(line.text, source_text, source_score, candidate_text, candidate_score)
+            decisions.append(
+                {
+                    "line": asdict(line),
+                    "source_text": source_text,
+                    "source_score": source_score,
+                    "candidate_text": candidate_text,
+                    "candidate_score": candidate_score,
+                    "preserve": preserve,
+                    "selected": not preserve,
+                }
+            )
+            if not preserve:
+                selected.append(line)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    mask_path = output.with_name(output.stem + "_mask.png")
+    manifest_common = {
+        "source": source.name,
+        "candidate": candidate.name,
+        "output": output.name,
+        "mask": mask_path.name,
+        "glyph_donor": glyph_donor.name if glyph_donor else None,
+        "glyph_feather": glyph_feather if restoration == "vae-glyphs" else None,
+        "restoration": restoration,
+        "selection": selection,
+        "erase_background": erase_background,
+        "composite_mask": composite_mask,
+        "box_source": "detector" if detect_boxes else "verified_annotations",
+        "annotation_boxes": annotation_boxes,
+        "decisions": decisions,
+    }
+    if not selected:
+        combined = np.zeros(source_rgb.shape[:2], dtype=np.uint8)
+        shutil.copyfile(candidate, output)
+        Image.fromarray(combined).save(mask_path)
+        payload = {
+            **manifest_common,
+            "mask_fraction": 0.0,
+            "source_glyph_fraction": 0.0,
+            "source_layer_fraction": 0.0,
+        }
+        _write_manifest(manifest, payload)
+        log.info("Copied %s unchanged because every line passed", output)
+        return
+    if restoration in {"source-silhouette", "vae-glyphs"}:
+        source_masks = [source_silhouette_mask(source_rgb, line.box, line.angle) for line in selected]
+        candidate_masks = [source_silhouette_mask(candidate_rgb, line.box, line.angle) for line in selected]
+        line_masks = []
+        for line, source_mask, candidate_mask in zip(selected, source_masks, candidate_masks, strict=True):
+            radius = 5 if line.box[3] - line.box[1] >= 48 else 3
+            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * radius + 1,) * 2)
+            line_masks.append(cv2.dilate(np.maximum(source_mask, candidate_mask), kernel))
+        masks = line_masks
+    else:
+        source_masks = [foreground_mask(source_rgb, line.box) for line in selected]
+        candidate_masks = [foreground_mask(candidate_rgb, line.box) for line in selected]
+        masks = [np.maximum(left, right) for left, right in zip(source_masks, candidate_masks, strict=True)]
+    del candidate_masks
+    groups = group_text_lines(selected)
+    if erase_background:
+        background = cv2.cvtColor(candidate_rgb, cv2.COLOR_RGB2BGR)
+        for group in groups:
+            background = region_eraser.erase_lama(
+                background,
+                np.maximum.reduce([masks[index] for index in group]),
+            )
+        background_rgb = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
+        residual_masks = [
+            residual_glyph_mask(background_rgb, mask, line.box) for line, mask in zip(selected, masks, strict=True)
+        ]
+        for group in groups:
+            residual = np.maximum.reduce([residual_masks[index] for index in group])
+            if np.any(residual):
+                background = region_eraser.erase_lama(background, residual)
+        background_rgb = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
+    else:
+        background_rgb = candidate_rgb
+        residual_masks = []
+    source_glyph_mask = np.maximum.reduce(source_masks)
+    source_layer_mask = source_glyph_mask
+    if restoration == "source-glyphs":
+        source_layer_mask = (
+            source_box_mask(source_rgb.shape[:2], [line.box for line in selected])
+            if composite_mask == "boxes"
+            else source_glyph_mask
+        )
+        restored = composite_source_glyphs(source_rgb, background_rgb, source_layer_mask, feather=3.0)
+        Image.fromarray(restored).save(output)
+    elif restoration in {"source-silhouette", "vae-glyphs"}:
+        restored = composite_fresh_text_edges(source_rgb, background_rgb, selected, source_masks)
+        if restoration == "vae-glyphs":
+            if donor_rgb is None:
+                raise RuntimeError("VAE glyph restoration requires a loaded donor")
+            restored = composite_reconstructed_glyphs(
+                donor_rgb,
+                restored,
+                source_layer_mask,
+                feather=glyph_feather,
+            )
+        Image.fromarray(restored).save(output)
+    else:
+        rendered = Image.fromarray(background_rgb)
+        for line, source_mask in zip(selected, source_masks, strict=True):
+            _render_line(rendered, line, _sample_text_color(source_rgb, source_mask, line.box))
+        rendered.save(output)
+    combined = np.maximum.reduce([*masks, *residual_masks])
+    if restoration == "source-glyphs" and not erase_background:
+        combined = source_layer_mask
+    Image.fromarray(combined).save(mask_path)
+    payload = {
+        **manifest_common,
+        "mask_fraction": float((combined > 0).mean()),
+        "source_glyph_fraction": float((source_glyph_mask > 0).mean()),
+        "source_layer_fraction": float((source_layer_mask > 0).mean()),
+    }
+    _write_manifest(manifest, payload)
+    log.info("Wrote %s with %.4f edited fraction", output, payload["mask_fraction"])
+
+
+if __name__ == "__main__":
+    main()
@@ -30,6 +30,8 @@ from remove_ai_watermarks._internal.watermark_profiles import resolve_seed
 if TYPE_CHECKING:
    from collections.abc import Callable

+    from remove_ai_watermarks._internal.text_restoration import VerifiedTextManifest
+
 log = logging.getLogger(__name__)

 QWEN_IMAGE_2512_MODEL_ID = "Qwen/Qwen-Image-2512"
@@ -973,6 +975,30 @@ class QwenZImagePipeline:
            result = result.resize(image.size, Image.Resampling.LANCZOS)
        return result.convert("RGB")

+    def _qwen_vae_roundtrip(self, image: Image.Image) -> Image.Image:
+        """Reconstruct source pixels through the already loaded Qwen VAE."""
+        import torch
+
+        pipe, _controlnet_input_cls = self._load_qwen()
+        source_width, source_height = image.size
+        pad_width = (-source_width) % 8
+        pad_height = (-source_height) % 8
+        padded = image.convert("RGB")
+        if pad_width or pad_height:
+            padded = Image.fromarray(
+                np.pad(
+                    np.asarray(padded),
+                    ((0, pad_height), (0, pad_width), (0, 0)),
+                    mode="edge",
+                )
+            )
+        pipe.load_models_to_device(["vae"])
+        tensor = pipe.preprocess_image(padded).to(device=self.device, dtype=self.torch_dtype)
+        with torch.inference_mode():
+            latents = pipe.vae.encode(tensor)
+            decoded = pipe.vae.decode(latents)
+        return pipe.vae_output_to_image(decoded).crop((0, 0, source_width, source_height)).convert("RGB")
+
    @staticmethod
    def _detail_size(
        crop_size: tuple[int, int],
@@ -1044,10 +1070,15 @@ class QwenZImagePipeline:
        tile: bool = False,
        tile_size: int = 1024,
        tile_overlap: int = 128,
+        text_manifest: VerifiedTextManifest | None = None,
    ) -> Image.Image:
        """Execute global regeneration and masked face repair."""
        self._require_cuda()
        seed = resolve_seed(seed)
+        donor = None
+        if text_manifest is not None:
+            self._progress("Reconstructing the verified text donor with the Qwen VAE...")
+            donor = self._qwen_vae_roundtrip(image)
        global_strength = (
            resolution_adaptive_denoise(image.width, image.height) if strength is None else float(strength)
        )
@@ -1068,14 +1099,28 @@ class QwenZImagePipeline:
        boxes = detect_faces(image)
        if not boxes:
            self._progress("No faces detected; keeping the Qwen global result.")
-            return global_result
-        masks = self._sam_masks(image, boxes)
-        face_strength = largest_face_denoise(boxes, image.size) * FACE_DENOISE_SCALE
-        return self._run_faces(
-            image,
-            global_result,
-            boxes,
-            masks,
-            strength=face_strength,
-            seed=seed,
+            result = global_result
+        else:
+            masks = self._sam_masks(image, boxes)
+            face_strength = largest_face_denoise(boxes, image.size) * FACE_DENOISE_SCALE
+            result = self._run_faces(
+                image,
+                global_result,
+                boxes,
+                masks,
+                strength=face_strength,
+                seed=seed,
+            )
+        if text_manifest is None:
+            return result
+        if donor is None:
+            raise RuntimeError("Verified text restoration requires a Qwen-VAE donor")
+        from remove_ai_watermarks._internal.text_restoration import (
+            blend_fidelity_anchor,
+            restore_verified_text,
        )
+
+        self._progress("Blending the Qwen-VAE fidelity anchor...")
+        anchor = blend_fidelity_anchor(result, donor)
+        self._progress(f"Restoring {len(text_manifest.lines)} verified text lines...")
+        return restore_verified_text(image, anchor, donor, text_manifest.lines)
@@ -0,0 +1,353 @@
+"""Opt-in restoration of verified text from a Qwen VAE reconstruction."""
+
+# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+import cv2
+import numpy as np
+from PIL import Image
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+    from pathlib import Path
+
+    from numpy.typing import NDArray
+
+TEXT_MANIFEST_SCHEMA = 1
+FIDELITY_BLEND_ALPHA = 0.15
+GLYPH_FEATHER = 0.5
+
+
+@dataclass(frozen=True)
+class VerifiedTextLine:
+    """One operator-verified source line in source-pixel coordinates."""
+
+    box: tuple[int, int, int, int]
+    text: str
+    script: str
+    angle: float = 0.0
+
+
+@dataclass(frozen=True)
+class VerifiedTextManifest:
+    """Text annotations cryptographically bound to one decoded RGB source."""
+
+    source_pixel_sha256: str
+    width: int
+    height: int
+    lines: tuple[VerifiedTextLine, ...]
+
+
+def source_pixel_sha256(image: Image.Image) -> str:
+    """Hash decoded RGB geometry and bytes, independent of container metadata."""
+    rgb = image.convert("RGB")
+    digest = hashlib.sha256()
+    digest.update(rgb.width.to_bytes(8, "big"))
+    digest.update(rgb.height.to_bytes(8, "big"))
+    digest.update(rgb.tobytes())
+    return digest.hexdigest()
+
+
+def load_verified_text_manifest(path: Path, source: Image.Image) -> VerifiedTextManifest:
+    """Load and validate a manually verified manifest for exactly ``source``."""
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        raise ValueError(f"Cannot read text manifest {path}: {exc}") from exc
+    if not isinstance(payload, dict):
+        raise ValueError("Text manifest must be a JSON object")
+    if payload.get("schema_version") != TEXT_MANIFEST_SCHEMA:
+        raise ValueError(f"Text manifest schema_version must be {TEXT_MANIFEST_SCHEMA}")
+    if payload.get("verified") is not True:
+        raise ValueError("Text manifest must contain verified=true after manual review")
+
+    rgb = source.convert("RGB")
+    width = _manifest_integer(payload, "width")
+    height = _manifest_integer(payload, "height")
+    if (width, height) != rgb.size:
+        raise ValueError(f"Text manifest dimensions {width}x{height} do not match source {rgb.width}x{rgb.height}")
+    expected_hash = payload.get("source_pixel_sha256")
+    if not isinstance(expected_hash, str) or len(expected_hash) != 64:
+        raise ValueError("Text manifest source_pixel_sha256 must be a 64-character SHA-256")
+    actual_hash = source_pixel_sha256(rgb)
+    if expected_hash.casefold() != actual_hash:
+        raise ValueError("Text manifest source_pixel_sha256 does not match the decoded source pixels")
+
+    raw_lines = payload.get("lines")
+    if not isinstance(raw_lines, list) or not raw_lines:
+        raise ValueError("Text manifest lines must be a non-empty list")
+    lines = tuple(_load_line(item, width, height, index) for index, item in enumerate(raw_lines))
+    if list(lines) != sorted(lines, key=lambda line: (line.box[1], line.box[0])):
+        raise ValueError("Text manifest lines must be in top-to-bottom, left-to-right reading order")
+    return VerifiedTextManifest(actual_hash, width, height, lines)
+
+
+def _manifest_integer(payload: dict[str, Any], key: str) -> int:
+    value = payload.get(key)
+    if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
+        raise ValueError(f"Text manifest {key} must be a positive integer")
+    return value
+
+
+def _load_line(item: Any, width: int, height: int, index: int) -> VerifiedTextLine:
+    if not isinstance(item, dict):
+        raise ValueError(f"Text manifest line {index} must be an object")
+    raw_box = item.get("box")
+    if (
+        not isinstance(raw_box, list)
+        or len(raw_box) != 4
+        or any(isinstance(value, bool) or not isinstance(value, int) for value in raw_box)
+    ):
+        raise ValueError(f"Text manifest line {index} box must contain four integers")
+    box = tuple(raw_box)
+    x1, y1, x2, y2 = box
+    if not (0 <= x1 < x2 <= width and 0 <= y1 < y2 <= height):
+        raise ValueError(f"Text manifest line {index} box is outside the source dimensions")
+    text = item.get("text")
+    script = item.get("script")
+    if not isinstance(text, str) or not text.strip():
+        raise ValueError(f"Text manifest line {index} text must be non-empty")
+    if not isinstance(script, str) or not script.strip():
+        raise ValueError(f"Text manifest line {index} script must be non-empty")
+    angle_value = item.get("angle", 0.0)
+    if isinstance(angle_value, bool) or not isinstance(angle_value, int | float):
+        raise ValueError(f"Text manifest line {index} angle must be numeric")
+    angle = float(angle_value)
+    if not math.isfinite(angle) or abs(angle) > 30.0:
+        raise ValueError(f"Text manifest line {index} angle must be between -30 and 30 degrees")
+    return VerifiedTextLine(box, text, script, angle)
+
+
+def blend_fidelity_anchor(clean: Image.Image, donor: Image.Image) -> Image.Image:
+    """Blend 15% Qwen-VAE reconstruction into the oracle-clean pipeline output."""
+    clean_rgb = np.asarray(clean.convert("RGB"), dtype=np.float32)
+    donor_rgb = np.asarray(donor.convert("RGB"), dtype=np.float32)
+    if clean_rgb.shape != donor_rgb.shape:
+        raise ValueError("Clean result and Qwen-VAE donor dimensions must match")
+    blended = np.rint(clean_rgb * (1.0 - FIDELITY_BLEND_ALPHA) + donor_rgb * FIDELITY_BLEND_ALPHA)
+    return Image.fromarray(np.clip(blended, 0, 255).astype(np.uint8))
+
+
+def restore_verified_text(
+    source: Image.Image,
+    candidate: Image.Image,
+    donor: Image.Image,
+    lines: tuple[VerifiedTextLine, ...],
+) -> Image.Image:
+    """Erase candidate glyphs, then composite verified Qwen-VAE glyph cores."""
+    from remove_ai_watermarks import region_eraser
+
+    if not region_eraser.lama_available():
+        raise RuntimeError(
+            "Verified text restoration requires LaMa. Install: pip install 'remove-ai-watermarks[text-restoration]'"
+        )
+    source_rgb = np.asarray(source.convert("RGB"))
+    candidate_rgb = np.asarray(candidate.convert("RGB"))
+    donor_rgb = np.asarray(donor.convert("RGB"))
+    if source_rgb.shape != candidate_rgb.shape or source_rgb.shape != donor_rgb.shape:
+        raise ValueError("Source, candidate, and Qwen-VAE donor dimensions must match")
+
+    source_masks = [source_silhouette_mask(source_rgb, line.box, line.angle) for line in lines]
+    for index, mask in enumerate(source_masks):
+        if not np.any(mask):
+            raise ValueError(f"Verified text line {index} produced no source glyph pixels")
+    candidate_masks = [source_silhouette_mask(candidate_rgb, line.box, line.angle) for line in lines]
+    erase_masks = []
+    for line, source_mask, candidate_mask in zip(lines, source_masks, candidate_masks, strict=True):
+        radius = 5 if line.box[3] - line.box[1] >= 48 else 3
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * radius + 1,) * 2)
+        erase_masks.append(cv2.dilate(np.maximum(source_mask, candidate_mask), kernel))
+    del candidate_masks
+
+    groups = group_text_lines(lines)
+    background = cv2.cvtColor(candidate_rgb, cv2.COLOR_RGB2BGR)
+    for group in groups:
+        background = region_eraser.erase_lama(background, np.maximum.reduce([erase_masks[index] for index in group]))
+    background_rgb = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
+    residual_masks = [
+        residual_glyph_mask(background_rgb, mask, line.box) for line, mask in zip(lines, erase_masks, strict=True)
+    ]
+    for group in groups:
+        residual = np.maximum.reduce([residual_masks[index] for index in group])
+        if np.any(residual):
+            background = region_eraser.erase_lama(background, residual)
+    del erase_masks, residual_masks
+    restored = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
+    restored = composite_fresh_text_edges(source_rgb, restored, lines, source_masks)
+    source_glyph_mask = np.maximum.reduce(source_masks)
+    restored = composite_reconstructed_glyphs(donor_rgb, restored, source_glyph_mask)
+    return Image.fromarray(restored)
+
+
+def source_silhouette_mask(
+    source_rgb: NDArray[Any],
+    box: tuple[int, int, int, int],
+    angle: float = 0.0,
+) -> NDArray[Any]:
+    """Recover a thresholded glyph shape without retaining source amplitudes."""
+    height, width = source_rgb.shape[:2]
+    x1, y1, x2, y2 = _clip_box(box, width, height)
+    gray = cv2.cvtColor(source_rgb[y1:y2, x1:x2], cv2.COLOR_RGB2GRAY)
+    support = np.ones(gray.shape, dtype=np.uint8)
+    if angle:
+        box_width, box_height = x2 - x1, y2 - y1
+        theta = math.radians(abs(angle))
+        cosine, sine = math.cos(theta), math.sin(theta)
+        denominator = cosine * cosine - sine * sine
+        rect_width = (box_width * cosine - box_height * sine) / denominator
+        rect_height = (box_height * cosine - box_width * sine) / denominator
+        rotated = cv2.boxPoints(
+            ((box_width / 2, box_height / 2), (max(1.0, rect_width * 0.92), max(1.0, rect_height * 0.62)), -angle)
+        )
+        support.fill(0)
+        cv2.fillConvexPoly(support, np.rint(rotated).astype(np.int32), 1)
+        values = gray[support > 0]
+        background_luma = float(np.median(values))
+    else:
+        ring_pad = max(6, min(20, (y2 - y1) // 4))
+        rx1, ry1, rx2, ry2 = _clip_box((x1, y1, x2, y2), width, height, pad=ring_pad)
+        context = cv2.cvtColor(source_rgb[ry1:ry2, rx1:rx2], cv2.COLOR_RGB2GRAY)
+        ring = np.ones(context.shape, dtype=bool)
+        ring[y1 - ry1 : y2 - ry1, x1 - rx1 : x2 - rx1] = False
+        background_luma = float(np.median(context[ring])) if ring.any() else float(np.median(gray))
+        values = gray.reshape(-1)
+    low, high = float(np.percentile(values, 2)), float(np.percentile(values, 98))
+    dark_contrast, light_contrast = background_luma - low, high - background_luma
+    threshold = max(16.0, min(56.0, max(light_contrast, dark_contrast) * 0.22))
+    if light_contrast > dark_contrast:
+        crop_mask = (gray.astype(np.float32) >= background_luma + threshold).astype(np.uint8) * 255
+    else:
+        crop_mask = (gray.astype(np.float32) <= background_luma - threshold).astype(np.uint8) * 255
+    crop_mask[support == 0] = 0
+    result = np.zeros((height, width), dtype=np.uint8)
+    result[y1:y2, x1:x2] = crop_mask
+    return result
+
+
+def residual_glyph_mask(
+    background_rgb: NDArray[Any],
+    original_mask: NDArray[Any],
+    box: tuple[int, int, int, int],
+) -> NDArray[Any]:
+    """Find glyph-like contrast left after the first inpaint pass."""
+    residual = _foreground_mask(background_rgb, box)
+    residual = cv2.bitwise_and(residual, original_mask)
+    return cv2.dilate(residual, np.ones((5, 5), np.uint8), iterations=1)
+
+
+def composite_fresh_text_edges(
+    source_rgb: NDArray[Any],
+    background_rgb: NDArray[Any],
+    lines: tuple[VerifiedTextLine, ...],
+    masks: list[NDArray[Any]],
+) -> NDArray[Any]:
+    """Render fresh antialiased edges for source-derived glyph masks."""
+    restored = background_rgb
+    for line, mask in zip(lines, masks, strict=True):
+        color = _sample_text_color(source_rgb, mask, line.box)
+        restored = composite_fresh_silhouette(restored, mask, color)
+    return restored
+
+
+def composite_reconstructed_glyphs(
+    donor_rgb: NDArray[Any],
+    background_rgb: NDArray[Any],
+    glyph_mask: NDArray[Any],
+    *,
+    feather: float = GLYPH_FEATHER,
+) -> NDArray[Any]:
+    """Composite an exact reconstructed core with a narrow donor edge."""
+    if donor_rgb.shape != background_rgb.shape or donor_rgb.shape[:2] != glyph_mask.shape:
+        raise ValueError("donor, background, and glyph mask dimensions must match")
+    blurred = cv2.GaussianBlur(glyph_mask, (0, 0), feather) if feather > 0 else glyph_mask
+    alpha = np.maximum(glyph_mask, blurred).astype(np.float32) / 255.0
+    combined = donor_rgb.astype(np.float32) * alpha[..., None] + background_rgb.astype(np.float32) * (
+        1.0 - alpha[..., None]
+    )
+    return np.clip(np.rint(combined), 0, 255).astype(np.uint8)
+
+
+def composite_fresh_silhouette(
+    background_rgb: NDArray[Any],
+    glyph_mask: NDArray[Any],
+    color: tuple[int, int, int],
+    *,
+    feather: float = 0.35,
+) -> NDArray[Any]:
+    """Render a binary source shape with fresh color and antialiasing."""
+    if background_rgb.shape[:2] != glyph_mask.shape:
+        raise ValueError("background and glyph mask dimensions must match")
+    antialiased = cv2.GaussianBlur(glyph_mask, (0, 0), feather) if feather > 0 else glyph_mask
+    alpha = antialiased.astype(np.float32)[..., None] / 255.0
+    foreground = np.empty_like(background_rgb)
+    foreground[:, :] = color
+    combined = foreground.astype(np.float32) * alpha + background_rgb.astype(np.float32) * (1.0 - alpha)
+    return np.clip(combined, 0, 255).astype(np.uint8)
+
+
+def _clip_box(box: tuple[int, int, int, int], width: int, height: int, pad: int = 0) -> tuple[int, int, int, int]:
+    x1, y1, x2, y2 = box
+    return max(0, x1 - pad), max(0, y1 - pad), min(width, x2 + pad), min(height, y2 + pad)
+
+
+def _foreground_mask(source_rgb: NDArray[Any], box: tuple[int, int, int, int]) -> NDArray[Any]:
+    height, width = source_rgb.shape[:2]
+    line_height = box[3] - box[1]
+    x1, y1, x2, y2 = _clip_box(box, width, height, pad=max(6, int(line_height * 0.12)))
+    gray = cv2.cvtColor(source_rgb[y1:y2, x1:x2], cv2.COLOR_RGB2GRAY)
+    ring_pad = max(8, min(24, (y2 - y1) // 5))
+    rx1, ry1, rx2, ry2 = _clip_box((x1, y1, x2, y2), width, height, pad=ring_pad)
+    context = cv2.cvtColor(source_rgb[ry1:ry2, rx1:rx2], cv2.COLOR_RGB2GRAY)
+    ring = np.ones(context.shape, dtype=bool)
+    ring[y1 - ry1 : y2 - ry1, x1 - rx1 : x2 - rx1] = False
+    background_luma = float(np.median(context[ring])) if ring.any() else float(np.median(gray))
+    low, high = float(np.percentile(gray, 4)), float(np.percentile(gray, 96))
+    dark_contrast, light_contrast = background_luma - low, high - background_luma
+    threshold = max(24.0, min(72.0, max(light_contrast, dark_contrast) * 0.32))
+    if light_contrast > dark_contrast:
+        mask = (gray.astype(np.float32) >= background_luma + threshold).astype(np.uint8) * 255
+    else:
+        mask = (gray.astype(np.float32) <= background_luma - threshold).astype(np.uint8) * 255
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((2, 2), np.uint8))
+    dilation = 5 if line_height >= 48 else 3
+    mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * dilation + 1,) * 2))
+    result = np.zeros((height, width), dtype=np.uint8)
+    result[y1:y2, x1:x2] = mask
+    return result
+
+
+def _sample_text_color(
+    source_rgb: NDArray[Any], mask: NDArray[Any], box: tuple[int, int, int, int]
+) -> tuple[int, int, int]:
+    height, width = source_rgb.shape[:2]
+    x1, y1, x2, y2 = _clip_box(box, width, height, pad=2)
+    crop = source_rgb[y1:y2, x1:x2]
+    pixels = crop[mask[y1:y2, x1:x2] > 0]
+    luma = pixels.mean(axis=1)
+    background_luma = float(crop[[0, -1], :, :].reshape(-1, 3).mean(axis=1).mean())
+    selected = (
+        pixels[luma <= np.percentile(luma, 20)] if background_luma >= 128 else pixels[luma >= np.percentile(luma, 80)]
+    )
+    return tuple(int(value) for value in np.median(selected, axis=0))
+
+
+def group_text_lines(lines: Sequence[VerifiedTextLine]) -> list[list[int]]:
+    """Group nearby same-script lines for a shared LaMa erase pass."""
+    groups: list[list[int]] = []
+    for index, line in enumerate(lines):
+        if not groups:
+            groups.append([index])
+            continue
+        previous = lines[groups[-1][-1]]
+        gap = line.box[1] - previous.box[3]
+        if line.script != previous.script or gap > max(60, int((previous.box[3] - previous.box[1]) * 1.1)):
+            groups.append([index])
+        else:
+            groups[-1].append(index)
+    return groups
@@ -26,6 +26,8 @@ if TYPE_CHECKING:
    from collections.abc import Callable
    from pathlib import Path

+    from remove_ai_watermarks._internal.text_restoration import VerifiedTextManifest
+
 logger = logging.getLogger(__name__)

 try:
@@ -187,6 +189,7 @@ class WatermarkRemover:
        tile: bool = False,
        tile_size: int = 1024,
        tile_overlap: int = 128,
+        text_manifest: VerifiedTextManifest | None = None,
    ) -> Path:
        """Regenerate image pixels and write the result without AI metadata.

@@ -203,6 +206,10 @@ class WatermarkRemover:
        resolved_strength = resolve_strength(strength, vendor, self.model_profile, size=source.size)
        if not 0.0 <= resolved_strength <= 1.0:
            raise ValueError(f"Strength must be between 0.0 and 1.0, got {resolved_strength}")
+        if text_manifest is not None and self.model_profile == SDXL_ZIMAGE_PROFILE:
+            raise ValueError("Verified text restoration is supported only by the qwen-zimage profile")
+        if text_manifest is not None and tile:
+            raise ValueError("Verified text restoration is not calibrated with tiled diffusion")

        result = self._load_qwen_zimage_pipeline().run(
            source,
@@ -211,6 +218,7 @@ class WatermarkRemover:
            tile=tile,
            tile_size=tile_size,
            tile_overlap=tile_overlap,
+            text_manifest=text_manifest,
        )
        self._write_output(result, destination)
        return destination
@@ -243,6 +243,7 @@ class InvisibleOptions:
    tile: bool = False
    tile_size: int = 1024
    tile_overlap: int = 128
+    text_manifest: Path | None = None


 # What the invisible stage did. "unavailable" is the one outcome the caller must
@@ -523,6 +524,7 @@ def _run_invisible(
        tile=opts.tile,
        tile_size=opts.tile_size,
        tile_overlap=opts.tile_overlap,
+        text_manifest=opts.text_manifest,
    )
    say("invisible", "removed")
    return "removed"
@@ -311,6 +311,16 @@ _cpu_offload_option = click.option(
    ),
 )

+_text_manifest_option = click.option(
+    "--text-manifest",
+    type=click.Path(exists=True, dir_okay=False, path_type=Path),
+    default=None,
+    help=(
+        "Experimental verified-text restoration manifest. Requires qwen-zimage, "
+        "the text-restoration extra, native untiled geometry, and no postprocessing."
+    ),
+)
+

 _visible_backend_option = click.option(
    "--backend",
@@ -787,6 +797,7 @@ def cmd_erase(
@_tile_options
@_force_option
@_cpu_offload_option
+@_text_manifest_option
@click.pass_context
 def cmd_invisible(
    ctx: click.Context,
@@ -806,6 +817,7 @@ def cmd_invisible(
    tile_overlap: int,
    force: bool,
    cpu_offload: bool,
+    text_manifest: Path | None,
 ) -> None:
    """Remove invisible AI watermarks (SynthID, StableSignature, TreeRing).

@@ -853,20 +865,25 @@ def cmd_invisible(
    console.print(f"  Strength: {_resolved_strength_for_display(source, strength, vendor, pipeline)}")

    t0 = time.monotonic()
-    result_path = engine.remove_watermark(
-        image_path=source,
-        output_path=output,
-        strength=strength,
-        seed=seed,
-        humanize=humanize,
-        unsharp=unsharp,
-        adaptive_polish=adaptive_polish,
-        max_resolution=max_resolution,
-        vendor=vendor,
-        tile=tile,
-        tile_size=tile_size,
-        tile_overlap=tile_overlap,
-    )
+    try:
+        result_path = engine.remove_watermark(
+            image_path=source,
+            output_path=output,
+            strength=strength,
+            seed=seed,
+            humanize=humanize,
+            unsharp=unsharp,
+            adaptive_polish=adaptive_polish,
+            max_resolution=max_resolution,
+            vendor=vendor,
+            tile=tile,
+            tile_size=tile_size,
+            tile_overlap=tile_overlap,
+            text_manifest=text_manifest,
+        )
+    except (OSError, RuntimeError, ValueError) as exc:
+        console.print(f"  Error: {exc}")
+        raise SystemExit(1) from exc
    elapsed = time.monotonic() - t0

    size_kb = result_path.stat().st_size / 1024
@@ -1410,6 +1427,7 @@ def cmd_identify(ctx: click.Context, source: Path, no_visible: bool, as_json: bo
@_tile_options
@_force_option
@_cpu_offload_option
+@_text_manifest_option
@click.pass_context
 def cmd_all(
    ctx: click.Context,
@@ -1431,6 +1449,7 @@ def cmd_all(
    tile_overlap: int,
    force: bool,
    cpu_offload: bool,
+    text_manifest: Path | None,
 ) -> None:
    """Remove ALL watermarks: visible + invisible + metadata.

@@ -1508,6 +1527,7 @@ def cmd_all(
                tile=tile,
                tile_size=tile_size,
                tile_overlap=tile_overlap,
+                text_manifest=text_manifest,
            ),
            force=force,
            progress=progress,
@@ -18,6 +18,7 @@ from typing import TYPE_CHECKING

 from ._internal.watermark_profiles import (
    DEFAULT_PROFILE,
+    QWEN_ZIMAGE_PROFILE,
    REMOVAL_MODULES,
    resolve_adaptive_polish,
    resolve_seed,
@@ -148,6 +149,7 @@ class InvisibleEngine:
        tile: bool = False,
        tile_size: int = 1024,
        tile_overlap: int = 128,
+        text_manifest: Path | None = None,
    ) -> Path:
        """Remove invisible watermark from an image.

@@ -180,6 +182,11 @@ class InvisibleEngine:
                Engages only when the long side exceeds ``tile_size``.
            tile_size: Tile dimension in px (default 1024).
            tile_overlap: Overlap between adjacent tiles in px (default 128).
+            text_manifest: Operator-verified text lines bound to the decoded source
+                pixels. Enables the experimental Qwen-VAE ``vae-glyphs`` post-pass.
+                Requires the ``text-restoration`` extra and the ``qwen-zimage``
+                profile. Incompatible with tiling, downscaling, humanize, unsharp,
+                and adaptive polish because those combinations are not calibrated.

        Returns:
            Path to the cleaned image.
@@ -189,6 +196,23 @@ class InvisibleEngine:
        seed = resolve_seed(seed)
        adaptive_polish = resolve_adaptive_polish(adaptive_polish, self._remover.model_profile)

+        if text_manifest is not None:
+            if self._remover.model_profile != QWEN_ZIMAGE_PROFILE:
+                raise ValueError("--text-manifest is supported only by the qwen-zimage profile")
+            if max_resolution != 0:
+                raise ValueError("--text-manifest requires --max-resolution 0")
+            if tile:
+                raise ValueError("--text-manifest is not calibrated with --tile")
+            if humanize > 0.0 or unsharp > 0.0 or adaptive_polish:
+                raise ValueError("--text-manifest requires humanize=0, unsharp=0, and adaptive polish disabled")
+            from remove_ai_watermarks import region_eraser
+
+            if not region_eraser.lama_available():
+                raise RuntimeError(
+                    "Verified text restoration requires LaMa. Install: "
+                    "pip install 'remove-ai-watermarks[text-restoration]'"
+                )
+
        from PIL import Image, ImageOps

        # Resolution policy: a max_resolution cap (0 = none) bounds memory on huge
@@ -205,6 +229,11 @@ class InvisibleEngine:
        # Full-res original, kept for the adaptive-polish detail target (image is
        # reassigned to the resized copy below; PIL resize returns a new object).
        reference_pil = image
+        verified_text = None
+        if text_manifest is not None:
+            from remove_ai_watermarks._internal.text_restoration import load_verified_text_manifest
+
+            verified_text = load_verified_text_manifest(text_manifest, reference_pil)

        # Both profiles run at the input's native geometry, so only the explicit max
        # cap can move it, and it can only ever scale down.
@@ -240,6 +269,7 @@ class InvisibleEngine:
                tile=tile,
                tile_size=tile_size,
                tile_overlap=tile_overlap,
+                text_manifest=verified_text,
            )

            # Post-processing chain: decode the diffusion output ONCE, apply the
@@ -245,6 +245,7 @@ class TestInvisibleOptionsMirrorTheEngine:
            tile=True,
            tile_size=768,
            tile_overlap=64,
+            text_manifest=tmp_path / "verified-lines.json",
        )
        seen: dict[str, object] = {}

@@ -20,7 +20,7 @@ import pytest
 _SCRIPTS = Path(__file__).resolve().parent.parent / "scripts"


-def _load_assign():
+def _load_module():
    # fidelity_metrics is a standalone PEP723 script, not an installed module; load it by
    # path with scripts/ on sys.path so its `_plain_console` shim import resolves.
    sys.path.insert(0, str(_SCRIPTS))
@@ -35,7 +35,15 @@ def _load_assign():
        pytest.skip(f"fidelity_metrics import deps missing: {exc}")
    finally:
        sys.path.remove(str(_SCRIPTS))
-    return mod.assign_faces_one_to_one
+    return mod
+
+
+def _load_assign():
+    return _load_module().assign_faces_one_to_one
+
+
+def test_cer_remains_case_sensitive() -> None:
+    assert _load_module()._cer("A", "a") == 1.0


 def test_distinct_faces_match_nearest() -> None:
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+SCRIPT = Path(__file__).parents[1] / "scripts/infer_text_lines.py"
+SPEC = importlib.util.spec_from_file_location("infer_text_lines", SCRIPT)
+assert SPEC is not None
+assert SPEC.loader is not None
+module = importlib.util.module_from_spec(SPEC)
+sys.modules[SPEC.name] = module
+SPEC.loader.exec_module(module)
+
+
+def test_choose_language_prefers_observed_unicode_script() -> None:
+    probes = {"en": ("gibberish", 0.9), "ru": ("пример", 0.9), "ch": ("example", 0.9)}
+    assert module.choose_language(probes) == "ru"
+
+    probes["ch"] = ("示例", 0.9)
+    assert module.choose_language(probes) == "ch"
+
+
+def test_stable_recognition_requires_agreement_and_confidence() -> None:
+    assert module.stable_recognition([("Sample text", 0.9), ("sample  text", 0.95)]) == "Sample text"
+    assert module.stable_recognition([("Sample", 0.9), ("Simple", 0.95)]) is None
+    assert module.stable_recognition([("Sample", 0.8), ("Sample", 0.95)]) is None
@@ -43,6 +43,76 @@ class TestInvisibleEngineInit:
        assert engine._preload_kwargs == {"global_only": True}


+class TestVerifiedTextMode:
+    """The experimental mode must fail before loading models on unmeasured inputs."""
+
+    @staticmethod
+    def _engine(profile: str = "qwen-zimage") -> InvisibleEngine:
+        engine = object.__new__(InvisibleEngine)
+        engine._progress_callback = None
+        engine._remover = SimpleNamespace(model_profile=profile)
+        return engine
+
+    def test_rejects_incompatible_pipeline_options(self, tmp_path):
+        import pytest
+
+        manifest = tmp_path / "manifest.json"
+        manifest.write_text("{}", encoding="utf-8")
+        cases = (
+            ("sdxl-zimage", {}, "qwen-zimage"),
+            ("qwen-zimage", {"max_resolution": 1024}, "max-resolution 0"),
+            ("qwen-zimage", {"tile": True}, "not calibrated"),
+            ("qwen-zimage", {"humanize": 1.0}, "humanize=0"),
+            ("qwen-zimage", {"adaptive_polish": True}, "polish disabled"),
+        )
+        for profile, kwargs, message in cases:
+            with pytest.raises(ValueError, match=message):
+                self._engine(profile).remove_watermark(
+                    tmp_path / "unused.png",
+                    text_manifest=manifest,
+                    **kwargs,
+                )
+
+    def test_loads_and_forwards_verified_manifest(self, tmp_path, monkeypatch):
+        import json
+
+        from remove_ai_watermarks import region_eraser
+        from remove_ai_watermarks._internal.text_restoration import source_pixel_sha256
+
+        source = tmp_path / "source.png"
+        output = tmp_path / "output.png"
+        image = Image.new("RGB", (48, 32), (10, 20, 30))
+        image.save(source)
+        manifest = tmp_path / "manifest.json"
+        manifest.write_text(
+            json.dumps(
+                {
+                    "schema_version": 1,
+                    "verified": True,
+                    "source_pixel_sha256": source_pixel_sha256(image),
+                    "width": 48,
+                    "height": 32,
+                    "lines": [{"box": [8, 8, 40, 24], "text": "Exact", "script": "alphabetic"}],
+                }
+            ),
+            encoding="utf-8",
+        )
+        seen = {}
+
+        def fake_remove(**kwargs):
+            seen.update(kwargs)
+            Image.open(kwargs["image_path"]).save(kwargs["output_path"])
+            return kwargs["output_path"]
+
+        engine = self._engine()
+        engine._remover.remove_watermark = fake_remove
+        monkeypatch.setattr(region_eraser, "lama_available", lambda: True)
+
+        engine.remove_watermark(source, output, text_manifest=manifest)
+
+        assert seen["text_manifest"].lines[0].text == "Exact"
+
+
 class TestNativeOutputSize:
    """Model-side latent-grid rounding must not change the public output size."""

@@ -579,6 +579,78 @@ def test_cli_qwen_zimage_keeps_profile_postprocess_default(tmp_image_path, monke
    assert mock_engine.remove_watermark.call_args.kwargs["adaptive_polish"] is True


+def test_cli_forwards_verified_text_manifest(tmp_image_path, tmp_path, monkeypatch):
+    from remove_ai_watermarks import cli
+
+    manifest = tmp_path / "manifest.json"
+    manifest.write_text("{}", encoding="utf-8")
+    mock_engine = MagicMock()
+    mock_engine.remove_watermark.return_value = tmp_image_path
+    monkeypatch.setattr("remove_ai_watermarks.invisible_engine.is_available", lambda: True)
+    monkeypatch.setattr("remove_ai_watermarks.invisible_engine.InvisibleEngine", MagicMock(return_value=mock_engine))
+
+    result = CliRunner().invoke(
+        cli.main,
+        ["invisible", str(tmp_image_path), "--text-manifest", str(manifest), "--force"],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert mock_engine.remove_watermark.call_args.kwargs["text_manifest"] == manifest
+
+
+def test_cli_reports_verified_text_manifest_errors(tmp_image_path, tmp_path, monkeypatch):
+    from remove_ai_watermarks import cli
+
+    manifest = tmp_path / "manifest.json"
+    manifest.write_text("{}", encoding="utf-8")
+    mock_engine = MagicMock()
+    mock_engine.remove_watermark.side_effect = ValueError("manifest pixels do not match")
+    monkeypatch.setattr("remove_ai_watermarks.invisible_engine.is_available", lambda: True)
+    monkeypatch.setattr("remove_ai_watermarks.invisible_engine.InvisibleEngine", MagicMock(return_value=mock_engine))
+
+    result = CliRunner().invoke(
+        cli.main,
+        ["invisible", str(tmp_image_path), "--text-manifest", str(manifest), "--force"],
+    )
+
+    assert result.exit_code == 1
+    assert "manifest pixels do not match" in result.output
+
+
+def test_no_face_path_still_runs_verified_text_restoration(monkeypatch):
+    from remove_ai_watermarks._internal import qwen_zimage_pipeline, text_restoration
+    from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline
+    from remove_ai_watermarks._internal.text_restoration import VerifiedTextLine, VerifiedTextManifest
+
+    pipeline = object.__new__(QwenZImagePipeline)
+    pipeline.device = "cuda"
+    pipeline.progress_callback = None
+    source = Image.new("RGB", (32, 32), (10, 20, 30))
+    donor = Image.new("RGB", (32, 32), (40, 50, 60))
+    global_result = Image.new("RGB", (32, 32), (70, 80, 90))
+    anchor = Image.new("RGB", (32, 32), (100, 110, 120))
+    restored = Image.new("RGB", (32, 32), (130, 140, 150))
+    pipeline._qwen_vae_roundtrip = MagicMock(return_value=donor)
+    pipeline._run_global = MagicMock(return_value=global_result)
+    monkeypatch.setattr(qwen_zimage_pipeline, "detect_faces", lambda _image: [])
+    blend = MagicMock(return_value=anchor)
+    restore = MagicMock(return_value=restored)
+    monkeypatch.setattr(text_restoration, "blend_fidelity_anchor", blend)
+    monkeypatch.setattr(text_restoration, "restore_verified_text", restore)
+    manifest = VerifiedTextManifest(
+        "0" * 64,
+        32,
+        32,
+        (VerifiedTextLine((4, 4, 20, 16), "Exact", "alphabetic"),),
+    )
+
+    result = pipeline.run(source, strength=0.1, seed=0, text_manifest=manifest)
+
+    assert result is restored
+    blend.assert_called_once_with(global_result, donor)
+    restore.assert_called_once_with(source, anchor, donor, manifest.lines)
+
+
 def test_watermark_remover_dispatches_to_full_pipeline(tmp_path, monkeypatch):
    from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover

@@ -601,6 +673,7 @@ def test_watermark_remover_dispatches_to_full_pipeline(tmp_path, monkeypatch):
    _, kwargs = runtime.run.call_args
    assert kwargs["strength"] == pytest.approx(0.084)
    assert kwargs["seed"] == 0
+    assert kwargs["text_manifest"] is None
    assert output.exists()


@@ -0,0 +1,181 @@
+from __future__ import annotations
+
+import importlib.util
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from remove_ai_watermarks._internal import text_restoration
+
+SCRIPT = Path(__file__).parents[1] / "scripts" / "selective_text_restoration.py"
+SPEC = importlib.util.spec_from_file_location("selective_text_restoration", SCRIPT)
+assert SPEC is not None
+assert SPEC.loader is not None
+module = importlib.util.module_from_spec(SPEC)
+sys.modules[SPEC.name] = module
+SPEC.loader.exec_module(module)
+
+
+def test_normalized_edit_distance_ignores_case_and_whitespace() -> None:
+    assert module.normalized_edit_distance(" Sample text ", "sample\ntext") == 0.0
+
+
+def test_preserve_requires_source_candidate_agreement() -> None:
+    assert module.should_preserve_line("clean text", "clean text", 0.9, "clean text", 0.9)
+    assert not module.should_preserve_line("clean text", "clean text", 0.9, "damaged", 0.9)
+
+
+def test_preserve_rejects_unreliable_source_recognition() -> None:
+    assert not module.should_preserve_line("expected", "unrelated", 0.9, "unrelated", 0.9)
+    assert not module.should_preserve_line("expected", "expected", 0.7, "expected", 0.9)
+
+
+def test_cjk_recognition_box_excludes_overlapping_neighbor_lines() -> None:
+    line = module.TextLine((1281, 650, 2357, 818), "每天都是一个新的机会。", "cjk")
+
+    assert module._recognition_box(line, 2816, 1536) == (1247, 650, 2458, 818)
+
+
+def test_latin_recognition_box_keeps_context_padding() -> None:
+    line = module.TextLine((100, 200, 300, 260), "Sample text", "latin")
+
+    assert module._recognition_box(line, 1000, 1000) == (40, 180, 360, 280)
+    assert module._recognition_box(line, 1000, 1000, 0.1) == (40, 192, 360, 268)
+
+
+def test_verified_lines_cover_each_ground_truth_string() -> None:
+    root = Path(__file__).parents[1]
+    lines = json.loads((root / "data/evaluations/fidelity/text-lines.json").read_text(encoding="utf-8"))
+    ground_truth = json.loads((root / "data/evaluations/fidelity/ground-truth.json").read_text(encoding="utf-8"))
+
+    assert lines.keys() == ground_truth.keys()
+    for source, expected in ground_truth.items():
+        observed = " ".join(line["text"] for line in lines[source])
+        assert module.normalize_text(observed) == module.normalize_text(expected)
+
+
+def test_group_word_boxes_merges_words_but_not_neighboring_lines() -> None:
+    boxes = [(10, 10, 30, 30), (32, 12, 60, 29), (10, 35, 50, 55)]
+
+    assert module.group_word_boxes(boxes) == [(10, 10, 60, 30), (10, 35, 50, 55)]
+
+
+def test_group_word_boxes_does_not_merge_distant_columns() -> None:
+    boxes = [(10, 10, 60, 30), (500, 11, 560, 31)]
+
+    assert module.group_word_boxes(boxes) == boxes
+
+
+def test_source_glyph_composite_keeps_masked_pixels_exact() -> None:
+    source = np.zeros((9, 9, 3), dtype=np.uint8)
+    source[:, :] = (220, 180, 40)
+    background = np.zeros((9, 9, 3), dtype=np.uint8)
+    background[:, :] = (10, 20, 30)
+    mask = np.zeros((9, 9), dtype=np.uint8)
+    mask[3:6, 3:6] = 255
+
+    result = module.composite_source_glyphs(source, background, mask, feather=0.7)
+
+    np.testing.assert_array_equal(result[3:6, 3:6], source[3:6, 3:6])
+    np.testing.assert_array_equal(result[0, 0], background[0, 0])
+
+
+def test_fresh_silhouette_uses_new_color_instead_of_source_pixels() -> None:
+    background = np.zeros((9, 9, 3), dtype=np.uint8)
+    background[:, :] = (10, 20, 30)
+    mask = np.zeros((9, 9), dtype=np.uint8)
+    mask[3:6, 3:6] = 255
+
+    result = text_restoration.composite_fresh_silhouette(background, mask, (220, 180, 40), feather=0)
+
+    assert np.all(result[3:6, 3:6] == (220, 180, 40))
+    np.testing.assert_array_equal(result[0, 0], background[0, 0])
+
+
+def test_fresh_silhouette_antialiasing_softens_binary_edges() -> None:
+    background = np.zeros((9, 9, 3), dtype=np.uint8)
+    background[:, :] = (10, 20, 30)
+    mask = np.zeros((9, 9), dtype=np.uint8)
+    mask[3:6, 3:6] = 255
+
+    result = text_restoration.composite_fresh_silhouette(background, mask, (220, 180, 40), feather=1.0)
+
+    assert np.all(result[3, 3] > background[3, 3])
+    assert np.all(result[3, 3] < (220, 180, 40))
+
+
+def test_reconstructed_glyphs_keep_exact_donor_core_and_fresh_edge() -> None:
+    donor = np.zeros((9, 9, 3), dtype=np.uint8)
+    donor[:, :] = (180, 140, 60)
+    background = np.zeros((9, 9, 3), dtype=np.uint8)
+    background[:, :] = (10, 20, 30)
+    mask = np.zeros((9, 9), dtype=np.uint8)
+    mask[3:6, 3:6] = 255
+
+    fresh_edge = text_restoration.composite_fresh_silhouette(background, mask, (220, 180, 40))
+    result = module.composite_reconstructed_glyphs(donor, fresh_edge, mask, feather=0.5)
+
+    np.testing.assert_array_equal(result[3:6, 3:6], donor[3:6, 3:6])
+    assert np.any(result[2, 3] != fresh_edge[2, 3])
+    np.testing.assert_array_equal(result[0, 0], background[0, 0])
+
+
+def test_source_silhouette_discards_foreground_amplitudes() -> None:
+    source = np.full((15, 15, 3), 20, dtype=np.uint8)
+    source[5:10, 6:9] = 230
+    source[6:9, 7] = 180
+
+    mask = module.source_silhouette_mask(source, (4, 4, 11, 11))
+
+    assert mask.dtype == np.uint8
+    assert set(np.unique(mask)) <= {0, 255}
+    assert mask[7, 7] == 255
+    assert mask[4, 4] == 0
+
+
+def test_rotated_source_silhouette_excludes_axis_aligned_corners() -> None:
+    source = np.full((80, 160, 3), 20, dtype=np.uint8)
+    source[10:70, 10:150] = 230
+
+    mask = module.source_silhouette_mask(source, (0, 0, 160, 80), angle=12)
+
+    assert mask[0, 0] == 0
+    assert mask[79, 159] == 0
+
+
+def test_source_box_mask_pads_and_clips_boxes() -> None:
+    mask = module.source_box_mask((20, 30), [(1, 2, 11, 10), (25, 15, 30, 20)])
+
+    assert mask.shape == (20, 30)
+    assert mask[0, 0] == 255
+    assert mask[19, 29] == 255
+    assert mask[0, 22] == 0
+
+
+def test_detect_line_boxes_fails_closed_on_count_mismatch() -> None:
+    class Engine:
+        def predict(self, _image):
+            return [{"rec_scores": [0.9], "rec_boxes": [[10, 10, 30, 30]]}]
+
+    with pytest.raises(module.click.ClickException, match="detected 1 source lines; expected exactly 2"):
+        module.detect_line_boxes(Engine(), np.zeros((50, 50, 3), dtype=np.uint8), expected_count=2)
+
+
+def test_residual_mask_is_limited_to_original_glyph_positions(monkeypatch) -> None:
+    from remove_ai_watermarks._internal import text_restoration
+
+    background = np.zeros((8, 8, 3), dtype=np.uint8)
+    original = np.zeros((8, 8), dtype=np.uint8)
+    original[3, 3] = 255
+    detected = np.zeros((8, 8), dtype=np.uint8)
+    detected[3, 3] = 255
+    detected[6, 6] = 255
+    monkeypatch.setattr(text_restoration, "_foreground_mask", lambda _image, _box: detected)
+
+    residual = module.residual_glyph_mask(background, original, (0, 0, 8, 8))
+
+    assert residual[3, 3] == 255
+    assert residual[6, 6] == 0
@@ -0,0 +1,128 @@
+"""Verified-text manifest and compositor tests without model downloads."""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+import pytest
+from PIL import Image, PngImagePlugin
+
+from remove_ai_watermarks._internal.text_restoration import (
+    FIDELITY_BLEND_ALPHA,
+    VerifiedTextLine,
+    blend_fidelity_anchor,
+    load_verified_text_manifest,
+    restore_verified_text,
+    source_pixel_sha256,
+)
+
+
+def _manifest(image: Image.Image) -> dict[str, object]:
+    return {
+        "schema_version": 1,
+        "verified": True,
+        "source_pixel_sha256": source_pixel_sha256(image),
+        "width": image.width,
+        "height": image.height,
+        "lines": [
+            {
+                "box": [8, 8, 40, 24],
+                "text": "Exact text",
+                "script": "alphabetic",
+                "angle": 0.0,
+            }
+        ],
+    }
+
+
+def test_pixel_hash_ignores_container_metadata(tmp_path) -> None:
+    image = Image.new("RGB", (48, 32), (10, 20, 30))
+    plain = tmp_path / "plain.png"
+    tagged = tmp_path / "tagged.png"
+    image.save(plain)
+    metadata = PngImagePlugin.PngInfo()
+    metadata.add_text("note", "different container bytes")
+    image.save(tagged, pnginfo=metadata)
+
+    with Image.open(plain) as left, Image.open(tagged) as right:
+        assert plain.read_bytes() != tagged.read_bytes()
+        assert source_pixel_sha256(left) == source_pixel_sha256(right)
+
+
+def test_verified_manifest_is_bound_to_source_pixels(tmp_path) -> None:
+    source = Image.new("RGB", (48, 32), (10, 20, 30))
+    path = tmp_path / "lines.json"
+    path.write_text(json.dumps(_manifest(source)), encoding="utf-8")
+
+    loaded = load_verified_text_manifest(path, source)
+
+    assert loaded.width == 48
+    assert loaded.height == 32
+    assert loaded.lines == (VerifiedTextLine((8, 8, 40, 24), "Exact text", "alphabetic", 0.0),)
+
+
+@pytest.mark.parametrize(
+    ("mutation", "message"),
+    [
+        ({"verified": False}, "verified=true"),
+        ({"source_pixel_sha256": "0" * 64}, "does not match"),
+        ({"width": 49}, "dimensions"),
+        ({"lines": []}, "non-empty"),
+    ],
+)
+def test_manifest_rejects_unverified_or_unbound_input(tmp_path, mutation, message) -> None:
+    source = Image.new("RGB", (48, 32), (10, 20, 30))
+    payload = _manifest(source)
+    payload.update(mutation)
+    path = tmp_path / "lines.json"
+    path.write_text(json.dumps(payload), encoding="utf-8")
+
+    with pytest.raises(ValueError, match=message):
+        load_verified_text_manifest(path, source)
+
+
+def test_fidelity_anchor_uses_the_calibrated_rounding() -> None:
+    clean = Image.fromarray(np.array([[[1, 2, 3], [100, 150, 200]]], dtype=np.uint8))
+    donor = Image.fromarray(np.array([[[255, 254, 253], [200, 100, 50]]], dtype=np.uint8))
+
+    result = np.asarray(blend_fidelity_anchor(clean, donor))
+    expected = np.rint(
+        np.asarray(clean, dtype=np.float32) * (1.0 - FIDELITY_BLEND_ALPHA)
+        + np.asarray(donor, dtype=np.float32) * FIDELITY_BLEND_ALPHA
+    ).astype(np.uint8)
+
+    assert np.array_equal(result, expected)
+
+
+def test_restoration_uses_lama_and_qwen_vae_core(monkeypatch) -> None:
+    from remove_ai_watermarks import region_eraser
+
+    source = np.full((40, 64, 3), 20, dtype=np.uint8)
+    source[12:24, 12:44] = 235
+    candidate = np.full_like(source, 30)
+    candidate[12:24, 12:44] = 150
+    donor = np.full_like(source, 40)
+    donor[12:24, 12:44] = (210, 220, 230)
+    calls: list[np.ndarray] = []
+
+    def fake_erase(image_bgr, mask):
+        calls.append(mask.copy())
+        output = image_bgr.copy()
+        output[mask > 0] = (30, 30, 30)
+        return output
+
+    monkeypatch.setattr(region_eraser, "lama_available", lambda: True)
+    monkeypatch.setattr(region_eraser, "erase_lama", fake_erase)
+
+    result = restore_verified_text(
+        Image.fromarray(source),
+        Image.fromarray(candidate),
+        Image.fromarray(donor),
+        (VerifiedTextLine((8, 8, 48, 28), "Exact text", "alphabetic"),),
+    )
+
+    restored = np.asarray(result)
+    assert calls
+    assert np.all(restored[16, 20] == donor[16, 20])
+    assert np.all(restored[0, 0] == candidate[0, 0])
@@ -597,7 +597,7 @@ name = "cuda-bindings"
 version = "13.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/51/6b/457ca12dad3ee9bfcc9a545cfd6b64b359ba49de40f776f6e028e678f262/cuda_bindings-13.3.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c5879712accf6e14bb01aa5e67440eb84998b8d104b509cc7a6dc0b8f656a474", size = 6053539, upload-time = "2026-05-29T23:11:43.19Z" },
@@ -630,43 +630,43 @@ wheels = [

 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-cuda-nvrtc", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cuda-runtime", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cufft = [
-    { name = "nvidia-cufft", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cufft", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cufile = [
-    { name = "nvidia-cufile", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cufile", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cuda-cupti", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 curand = [
-    { name = "nvidia-curand", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-curand", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cusolver = [
-    { name = "nvidia-cublas", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-cusolver", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-cusparse", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cublas", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-cusolver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
-    { name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cusparse", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-nvjitlink", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" },
+    { name = "nvidia-nvtx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]

 [[package]]
@@ -1094,15 +1094,15 @@ name = "lightning"
 version = "2.6.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "fsspec", extra = ["http"] },
-    { name = "lightning-utilities" },
-    { name = "packaging" },
-    { name = "pytorch-lightning" },
-    { name = "pyyaml" },
-    { name = "torch" },
-    { name = "torchmetrics" },
-    { name = "tqdm" },
-    { name = "typing-extensions" },
+    { name = "fsspec", extra = ["http"], marker = "python_full_version < '3.13'" },
+    { name = "lightning-utilities", marker = "python_full_version < '3.13'" },
+    { name = "packaging", marker = "python_full_version < '3.13'" },
+    { name = "pytorch-lightning", marker = "python_full_version < '3.13'" },
+    { name = "pyyaml", marker = "python_full_version < '3.13'" },
+    { name = "torch", marker = "python_full_version < '3.13'" },
+    { name = "torchmetrics", marker = "python_full_version < '3.13'" },
+    { name = "tqdm", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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@@ -2604,6 +2604,21 @@ qwen-zimage = [
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