Add geometry draft mode and pad silhouette descenders

pre-commit: 1 maintain.sh - not rerun full, ruff + 34 targeted tests passed; 2) /simplify - pad lives in silhouette crop, stable=False reuses probes; 3) docs sync - no README/cli refs to update; 4) CLAUDE.md - no change
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)

src/remove_ai_watermarks/_internal/text_restoration.py

@@ -185,6 +185,20 @@ def restore_verified_text(
     return Image.fromarray(restored)
 
 
+def _glyph_crop_box(box: tuple[int, int, int, int], width: int, height: int) -> tuple[int, int, int, int]:
+    """Widen the detector box so descenders stay inside the silhouette crop.
+
+    Paddle line boxes sit 2-5 px above the true ink bottom on the poster
+    fixtures. The crop is the box itself, so those pixels never reached the
+    donor composite. Expand only on Y: 8% up, 25% down, clamped to the frame.
+    """
+    x1, y1, x2, y2 = box
+    line_h = max(1, y2 - y1)
+    pad_top = max(1, round(line_h * 0.08))
+    pad_bot = max(2, round(line_h * 0.25))
+    return max(0, x1), max(0, y1 - pad_top), min(width, x2), min(height, y2 + pad_bot)
+
+
 def source_silhouette_mask(
     source_rgb: NDArray[Any],
     box: tuple[int, int, int, int],
@@ -192,7 +206,7 @@ def source_silhouette_mask(
 ) -> 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)
+    x1, y1, x2, y2 = _glyph_crop_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:

src/remove_ai_watermarks/text_draft.py

@@ -223,15 +223,22 @@ def draft_text_lines(
     min_score: float = 0.85,
     detector: Any | None = None,
     engines: dict[str, Any] | None = None,
+    stable: bool = True,
 ) -> TextDraft:
     """Propose verified-text manifest lines for ``image``; never verified ones.
 
     Args:
         image: path of the source image (draft boxes are in ITS pixel space).
-        min_score: recognition confidence floor for every jittered read.
+        min_score: recognition confidence floor. With ``stable=True`` every
+            jittered read must clear it; with ``stable=False`` the single
+            probe for the chosen language must.
         detector/engines: injectable Paddle objects (tests use fakes); when
             None they are built from the ``text-draft`` extra, which must be
             installed (``draft_available()`` reports it).
+        stable: when True (default), accept a line only if three crop paddings
+            normalize identically. When False, take one recognition trio and
+            accept on score alone. Automatic restoration uses box/script only,
+            so the jitter gate is optional there.
 
     Returns:
         ``TextDraft`` with crop-stable ``accepted`` proposals and unstable
@@ -262,8 +269,14 @@ def draft_text_lines(
             probes[language] = _recognize(engine, source_rgb, box, script, 0.1)
         language = choose_language(probes)
         script = "cjk" if language == "ch" else "alphabetic"
-        reads = [_recognize(engines[language], source_rgb, box, script, ratio) for ratio in JITTER_RATIOS]
-        text = stable_recognition(reads, min_score)
+        if stable:
+            reads = [_recognize(engines[language], source_rgb, box, script, ratio) for ratio in JITTER_RATIOS]
+            text = stable_recognition(reads, min_score)
+        else:
+            text, score = probes[language]
+            reads = [(text, score)]
+            if not text.strip() or score < min_score:
+                text = None
         if text is None:
             rejected.append(
                 RejectedLine(