- 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>
