Nomad changes

.ai-rulez/domains/ocr-integration/DOMAIN.md

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+---
+description: OCR backend integration and image processing
+---
+
+- Multiple backends: Tesseract (C FFI via leptonica/tesseract-sys), PaddleOCR (ONNX Runtime), Python backends (EasyOCR, Surya) via FFI
+- Backend selection: priority-based with fallback — Tesseract default, PaddleOCR for CJK, Python backends as fallback
+- Image preprocessing: deskew, binarization, noise removal, contrast enhancement — applied before OCR
+- PSM modes: configurable page segmentation (single block, single line, sparse text) per use case
+- Table detection: identify table regions → cell extraction → row/column reconstruction → Markdown table output
+- hOCR: parse Tesseract hOCR output for word-level bounding boxes, confidence scores, reading order
+- Language management: auto-detect document language, load appropriate Tesseract traineddata, support multi-language documents
+- Caching: cache OCR results by image hash + backend + language + PSM mode
+- Confidence tracking: per-word and per-page confidence scores, flag low-confidence regions for review

.ai-rulez/domains/ocr-integration/agents/ocr-engineer.md

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+---
+name: ocr-engineer
+description: OCR pipeline development, backend integration, and table reconstruction
+model: haiku
+---
+
+When working on OCR code:
+
+1. Key source paths: crates/kreuzberg/src/ocr/ (processor.rs, tesseract_backend.rs, hocr.rs, cache.rs, language_registry.rs, table/)
+2. The OCR pipeline: Image Detection -> Preprocessing (denoise, deskew, binarize) -> Backend Selection -> OCR Execution -> hOCR Parsing -> Table Reconstruction -> Caching -> Return
+3. Backends: Tesseract (default, native C FFI via leptess), PaddleOCR (ONNX via ort), EasyOCR (Python via PyO3)
+4. For Python backends: use tokio::task::spawn_blocking, minimize GIL hold time with py.allow_threads(), cache Python data in Rust fields
+5. For table detection: detect via line/cell boundary detection, validate grid structure, OCR each cell, output as markdown
+6. For language management: validate against LanguageRegistry, check tessdata availability
+7. Cache OCR results with key = hash(image_bytes + language + config)
+8. hOCR parsing: use the hocr module to extract word-level bounding boxes and confidence scores

.ai-rulez/domains/ocr-integration/rules/ocr-backend-standards.md

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+---
+priority: critical
+---
+
+- Pluggable backend architecture: all backends implement the OcrBackend trait
+- Backend independence: switching backends must not require API changes
+- Tesseract is the default backend (native C FFI via leptess)
+- Python backends (EasyOCR, PaddleOCR): use tokio::task::spawn_blocking, release GIL for Rust work
+- Graceful degradation: if preferred backend unavailable, fall back to next available
+- All backends must return structured results with confidence scores
+- Document installation requirements and troubleshooting for each backend

.ai-rulez/domains/ocr-integration/rules/ocr-language-and-config.md

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+---
+priority: medium
+---
+
+- Validate language packs exist before OCR execution — fail fast with helpful message
+- Support ISO 639 language codes, map to backend-specific formats
+- Configuration cascade: CLI args > environment > config file > defaults
+- Provide troubleshooting guides for common issues (missing tessdata, backend not found)
+- Language pack installation: document per-platform instructions

.ai-rulez/domains/ocr-integration/rules/ocr-performance.md

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+---
+priority: high
+---
+
+- Cache OCR results: key = hash(image_bytes + language + config)
+- Invalidate cache when OCR config changes (backend, language, PSM mode)
+- Batch processing: process multiple images concurrently with configurable parallelism
+- Resource management: limit concurrent OCR operations to avoid memory exhaustion
+- Performance targets: <2s for single page, <10s for 10-page document
+- Monitor and log OCR processing times for regression detection

.ai-rulez/domains/ocr-integration/rules/ocr-quality.md

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+---
+priority: high
+---
+
+- Track confidence scores on all OCR results — expose in API
+- Image preprocessing (denoise, deskew, binarize) should improve accuracy by 10-30%
+- PSM mode selection: auto-detect layout, allow user override (single block, single line, sparse text, etc.)
+- Language detection: validate requested languages are available, provide install hints if not
+- Multi-language support: allow multiple languages per OCR request
+- Test OCR accuracy against ground-truth documents in CI

.ai-rulez/domains/ocr-integration/rules/ocr-table-and-hocr.md

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+---
+priority: high
+---
+
+- hOCR parsing: extract word-level bounding boxes, confidence scores, and text content
+- Preserve spatial relationships from hOCR output for layout reconstruction
+- Table detection: use cell boundary detection (line detection + intersection analysis)
+- Validate grid structure before treating detected regions as tables
+- OCR each cell individually for better accuracy
+- Convert tables to markdown format with proper column alignment