feat(ocr): add guided OCR mode using existing annotation regions

When a document has manually drawn annotation boxes, the user can now
enable "Nur annotierte Bereiche" in the OCR trigger panel. The engine
skips layout detection entirely and runs recognition only within the
pre-drawn bounding boxes, preserving manual transcription blocks.

- Python: adds OcrRegion model, extend OcrRequest/OcrBlock; guided
  branch in /ocr/stream groups by page and crops each region
- Engines: add extract_region_text() to both Kraken and Surya
- Java: adds OcrBlockResult.annotationId, OcrClient.OcrRegion,
  TriggerOcrDTO.useExistingAnnotations; OcrAsyncRunner dispatches to
  upsertGuidedBlock when annotationId is present; OcrService threads
  the flag through to runSingleDocument
- TranscriptionService: adds upsertGuidedBlock (creates, updates OCR,
  or preserves MANUAL blocks)
- Frontend: guided OCR toggle in OcrTrigger shown when blocks exist;
  skips destructive-replace confirmation in guided mode

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

ocr-service/engines/surya.py

@@ -81,6 +81,25 @@ def extract_page_blocks(image, page_idx: int, language: str = "de") -> list[dict
     return blocks
 
 
+def extract_region_text(image, x: float, y: float, w: float, h: float) -> str:
+    """Crop image to a normalized region and run Surya 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].
+    """
+    load_models()
+
+    pw, ph = image.size
+    x1 = max(0, int(x * pw))
+    y1 = max(0, int(y * ph))
+    x2 = min(pw, int((x + w) * pw))
+    y2 = min(ph, int((y + h) * ph))
+    crop = image.crop((x1, y1, x2, y2))
+
+    predictions = _recognition_predictor([crop], det_predictor=_detection_predictor)
+    return " ".join(line.text for line in predictions[0].text_lines)
+
+
 def extract_blocks(images: list, language: str = "de") -> list[dict]:
     """Run Surya OCR on a list of PIL images (one per page).