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

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

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

config.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"

Output

Each chunk is a Markdown file with YAML frontmatter:

---
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