After each training run, the Character Error Rate (CER = 1 - accuracy),
loss, accuracy, and epoch count are now stored on the OcrTrainingRun
record and shown in the training history table.
Also adds the missing POST /api/ocr/segtrain endpoint and the
triggerSegTraining service method so the segmentation training card
can actually trigger training.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
- Add /segtrain endpoint to OCR service (ZIP upload, ketos.segtrain,
backup rotation, in-process model reload)
- Add segtrainModel() to OcrClient and RestClientOcrClient (10-min timeout,
X-Training-Token header)
- Add SegmentationTrainingExportService: PAGE XML export with polygon
de-normalization and per-page PNG rendering via PDFBox
- Add GET /api/ocr/segmentation-training-data/export endpoint
- Make TranscriptionBlock.text nullable for segmentation-only blocks
(V31 migration)
- Add Paraglide i18n translation keys for all training UI strings (de/en/es)
- Pass source prop from TranscriptionEditView to TranscriptionBlock
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- POST /train in ocr-service with ZIP Slip validation, TemporaryDirectory,
ketos transfer learning, timestamped backups (keep last 3), in-process reload
- X-Training-Token auth (no-op in dev when TRAINING_TOKEN env is empty)
- trainModel() in OcrClient interface + RestClientOcrClient (10-min timeout,
multipart upload, forwards X-Training-Token when configured)
- TRAINING_TOKEN env var wired in docker-compose; --workers 2 in Dockerfile
so /health stays responsive during synchronous training
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Validates PDF download URLs against an ALLOWED_PDF_HOSTS allowlist
(default: minio,localhost,127.0.0.1) and disables redirect following
to prevent redirect-based SSRF.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The PDF viewer uses 1-based currentPage (starting at 1) but the OCR
engines produced 0-based pageNumber from enumerate(). Annotations
created by OCR were assigned to page 0, which doesn't exist in the
viewer. Change enumerate() to start=1 in both engines and the
streaming endpoint.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Streams one JSON line per completed page instead of buffering the
entire result. Emits start/page/error/done events. On per-page
failure, logs the traceback but yields a generic error message and
continues with the next page. Adds X-Accel-Buffering: no and
Cache-Control: no-cache headers for reverse-proxy compatibility.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
OCR engines are CPU-bound and were blocking Uvicorn's single async
event loop, making /health unresponsive during processing. This caused
new OCR requests to fail silently (health check failure → no DB record
→ UI shows NONE). Wrap engine calls in asyncio.to_thread() to keep the
event loop free. Also surface OCR trigger errors in the frontend
instead of silently resetting the spinner.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Pydantic v2 Field(alias=...) doesn't work with FastAPI as expected.
The Java client sends camelCase (pdfUrl, scriptType, pageNumber).
Use camelCase field names directly instead of aliases.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Surya models lazy-load on first OCR request instead of at startup
(saves ~3-4GB idle RAM — Kraken stays eager at ~16MB)
- Process one page at a time in Surya engine (limits peak memory)
- RECOGNITION_BATCH_SIZE=1, DETECTOR_BATCH_SIZE=1 (slower but fits in RAM)
- Revert mem_limit back to 6GB (sufficient with these optimizations)
- Render DPI stays at 200
Idle memory: ~2GB (Kraken only). Peak during OCR: ~5-6GB (Surya loaded).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Surya 0.17 models use ~5GB idle. At 300 DPI on a multi-page PDF,
page images + inference tensors push past the 6GB limit, causing
OOM kills during 'Detecting bboxes'. Increased to 10GB and reduced
render DPI to 200 (still sufficient for OCR, uses ~44% less memory).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The OCR service was getting 403 Forbidden because it tried to
download PDFs from MinIO using plain internal URLs without
authentication. MinIO buckets are private.
- Add S3Presigner bean to MinioConfig
- FileService.generatePresignedUrl(): generates 15-min presigned URLs
- OcrService uses presigned URLs instead of plain internal URLs
- Remove unused s3InternalUrl / bucketName @Value fields from OcrService
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New confidence.py module with two functions:
- apply_confidence_markers(): replaces words below threshold with
[unleserlich], collapses adjacent markers into one
- words_from_characters(): reconstructs word-level confidence from
Kraken's character-level data
Surya 0.17 provides native word-level confidence via line.words.
Kraken 7.0 provides per-character confidences via record.confidences.
Both engines now pass word+confidence data through main.py, which
applies the marker post-processing before returning the API response.
Threshold configurable via OCR_CONFIDENCE_THRESHOLD env var (default 0.3).
Frontend already renders [unleserlich] markers via transcriptionMarkers.ts.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Python microservice (ocr-service/):
- FastAPI app with /ocr and /health endpoints
- Surya engine: transformer-based OCR for typewritten/modern handwriting
- Kraken engine: historical HTR for Kurrent/Suetterlin with
pure-Python polygon-to-quad approximation (gift wrapping + rotating calipers)
- Eager model loading at startup via lifespan context manager
- PDF download via httpx, page rendering via pypdfium2 at 300 DPI
Java RestClientOcrClient:
- Implements OcrClient + OcrHealthClient interfaces
- Calls Python service via Spring RestClient
- Health check with graceful fallback
Docker Compose:
- New ocr-service container (mem_limit 6g, no host ports)
- Health check with start_period 60s for model loading
- ocr_models volume for Kraken model files
- Backend depends on ocr-service health
Refs #226, #227
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
