OCR-to-RAG pipeline for life science screenshots

PaddleOCR with preprocessing, scispaCy NER, figure/table detection,
citation extraction, and chunked Markdown output with frontmatter.
Includes watch mode and notebook reprocessing.

README.md

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+# OCR Pipeline for Life Science Screenshots
+
+Turns scientific screenshots into RAG-ready Markdown, with metadata attached.
+
+- OCR via PaddleOCR, falls back to Tesseract
+- Preprocessing: deskew, denoise, CLAHE, line removal
+- Figure/table detection
+- Entity extraction with scispaCy
+- Citation matching
+- Chunking for embedding
+
+## Quick start
+
+```bash
+uv sync           # or: pip install -e .
+pre-commit install
+
+ocr-pipeline run                  # process screenshots
+ocr-pipeline watch                # watch a folder
+ocr-pipeline reprocess ocr_sc.ipynb   # pull screenshots out of a notebook
+ocr-pipeline status
+```
+
+## Docker
+
+```bash
+docker build -t ocr-pipeline .
+
+docker run -v ~/Pictures:/data/screenshots -v ./data:/app/data ocr-pipeline run
+docker run -v ~/Pictures:/data/screenshots -v ./data:/app/data ocr-pipeline watch
+```
+
+## Config
+
+<details>
+<summary><code>config.yaml</code></summary>
+
+```yaml
+input:
+  paths: ["~/Pictures", "/mnt/storage3/aman/screenshots"]
+  patterns: ["SCR-*.png", "*.jpg", "*.jpeg", "*.tiff"]
+  recursive: true
+
+ocr:
+  engine: "paddleocr"  # paddleocr | tesseract | auto
+  languages: ["en", "latin"]
+  use_gpu: false
+  preprocess:
+    deskew: true
+    denoise: true
+    clahe: true
+    adaptive_threshold: true
+    remove_lines: true
+
+processing:
+  workers: 4
+  batch_size: 10
+  retry_attempts: 2
+
+detectors:
+  figures:
+    enabled: true
+    confidence_threshold: 0.7
+  tables:
+    enabled: true
+    confidence_threshold: 0.7
+
+entities:
+  enabled: true
+  model: "en_core_sci_lg"
+
+citations:
+  regex_enabled: true
+  grobid_enabled: false  # set true if you're running a GROBID server
+
+chunking:
+  chunk_size: 1000
+  chunk_overlap: 200
+
+output:
+  base_directory: "./data/ocr_output"
+  organize_by: "date_run"  # date_run | source_dir | flat
+  frontmatter:
+    - source_path
+    - source_hash
+    - timestamp
+    - ocr_engine
+    - ocr_confidence_mean
+    - language
+    - detected_entities
+    - has_figures
+    - has_tables
+    - citations_found
+    - chunk_index
+    - total_chunks
+
+watch:
+  enabled: true
+  debounce_seconds: 5
+  db_path: "./data/processed_files.db"
+```
+
+</details>
+
+## Output
+
+Each chunk is a Markdown file with YAML frontmatter:
+
+```markdown
+---
+source_path: "/Users/Aman/Pictures/SCR-20250115-gel.png"
+source_hash: "a1b2c3d4e5f6..."
+timestamp: "2025-01-15T10:30:00Z"
+ocr_engine: "paddleocr"
+ocr_confidence_mean: 0.91
+language: "en"
+detected_entities: ["GENE", "PROTEIN", "CHEMICAL"]
+has_figures: true
+has_tables: false
+citations_found: ["DOI:10.1038/nature12345", "PMID:12345678"]
+chunk_index: 0
+total_chunks: 2
+---
+
+# Screenshot: SCR-20250115-gel.png
+
+## Figures
+
+### Figure 1
+- BBox: [100, 200, 800, 600]
+- Confidence: 0.92
+- Caption: "Western blot showing BRCA1 expression..."
+
+## Detected Entities
+
+- BRCA1
+- CRISPR
+- β-actin
+
+## Citations
+
+- DOI: 10.1038/nature12345
+- PMID: 12345678
+
+## Extracted Text
+
+**Western Blot Analysis of BRCA1 Expression**
+
+Lane 1: WT control
+Lane 2: BRCA1 KO (CRISPR)
+Lane 3: BRCA1 KO + pBRCA1-WT rescue
+Lane 4: BRCA1 KO + pBRCA1-C61G mutant
+
+Anti-BRCA1 (1:1000), Anti-β-actin (1:5000)
+```
+
+Files land in `data/ocr_output/<date>/run_NNN/`.
+
+## Life science specifics
+
+Gene/protein names go through scispaCy's `en_core_sci_lg`. Chemical formulas and units (µM, ng/mL, kb/Mb/Gb, °C, ×g) get normalized, scientific notation gets cleaned up (`1.5×10⁻³` → `1.5×10^-3`), and gel/blot figures get their captions pulled out separately. Citations are matched by regex for DOI, PMID, arXiv, PMC, and ISBN.
+
+## Models
+
+First run downloads what it needs, cached in `~/.cache/ocr_pipeline/`:
+
+- PaddleOCR models (~200MB)
+- scispaCy `en_core_sci_lg` (~800MB)
+- Table Transformer (~500MB)
+- LayoutParser PubLayNet (~300MB)
+
+## TODO
+
+- [ ] Build a knowledge graph from extracted entities/citations
+