fix(ocr): use synthetic baseline in guided OCR to avoid blla crash on small crops

blla.segment() is a full-page layout detection model that kills the worker
process when called on tiny annotation crops (e.g. 597x89 px). For guided
OCR the annotation region IS already the text line, so segmentation is
unnecessary. Replace the blla call with a single synthetic BaselineLine that
spans the full crop width — rpred then runs recognition on the whole crop.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ocr-service/engines/kraken.py

@@ -82,10 +82,13 @@ def extract_page_blocks(image, page_idx: int, language: str = "de") -> list[dict
 def extract_region_text(image, x: float, y: float, w: float, h: float) -> str:
     """Crop image to a normalized region and run Kraken recognition on the crop.
 
-    Used for guided OCR — skips full-page layout detection and only processes
-    the given bounding box. Coordinates are normalized to [0, 1].
+    Used for guided OCR — skips full-page layout detection entirely.
+    A single synthetic baseline spanning the full crop width is used so that
+    blla.segment() (which crashes on small crops) is never called.
+    Coordinates are normalized to [0, 1].
     """
-    from kraken import blla, rpred
+    from kraken import rpred
+    from kraken.containers import Segmentation, BaselineLine
 
     if _model is None:
         raise RuntimeError("Kraken model is not loaded")
@@ -97,8 +100,28 @@ def extract_region_text(image, x: float, y: float, w: float, h: float) -> str:
     y2 = min(ph, int((y + h) * ph))
     crop = image.crop((x1, y1, x2, y2))
 
-    baseline_seg = blla.segment(crop)
-    pred_it = rpred.rpred(_model, crop, baseline_seg)
+    cw, ch = crop.size
+    if cw == 0 or ch == 0:
+        return ""
+
+    # Single synthetic baseline at vertical midpoint, spanning full crop width
+    mid_y = ch // 2
+    synthetic_seg = Segmentation(
+        type="baselines",
+        imagename="",
+        text_direction="horizontal-lr",
+        script_detection=False,
+        lines=[
+            BaselineLine(
+                id="line0",
+                baseline=[(0, mid_y), (cw, mid_y)],
+                boundary=[(0, 0), (cw, 0), (cw, ch), (0, ch)],
+            )
+        ],
+        regions={},
+        line_orders=[],
+    )
+    pred_it = rpred.rpred(_model, crop, synthetic_seg)
     return " ".join(r.prediction for r in pred_it)