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 (DOI, PMID, arXiv, ISBN)
• Chunking for embedding
• Knowledge graph over the output (kg commands)

Quick start

uv sync
uv run ocr-pipeline setup     # installs Tesseract if missing
uv run ocr-pipeline init      # write a config
uv run ocr-pipeline demo      # check it works
uv run ocr-pipeline run       # process your screenshots

Default install is small: Tesseract only, no multi-GB ML downloads.

Commands

• run / watch — process a directory, or watch for new screenshots
• status / stats — what's been processed
• doctor — check engines, models, config
• kg build|stats|query|export — knowledge graph over the output
• setup [basic|full] — install dependencies step by step

Use --force to reprocess. --exclude "*.photoslibrary/*" keeps recursive scans out of macOS photo bundles.

Optional extras

Heavy ML features are opt-in and degrade gracefully when missing (logged, step skipped, run continues):

Extra	Provides
paddle	PaddleOCR engine
scientific	spaCy/scispaCy NER
tables	Table Transformer detection
figures	LayoutParser figure/caption detection
full	All of the above
kg	Knowledge graph (networkx, txtai, litellm)
neo4j	Neo4j export driver

uv sync --extra full
# or guided:
uv run ocr-pipeline setup full

en_core_sci_lg is distributed separately from scispaCy and needs spaCy 3.7.x. If the model install conflicts, use a dedicated Python 3.11 venv and enable entities only there.

Knowledge graph

Build a graph from pipeline output: documents, chunks, entities, citations, entity co-occurrence.

uv sync --extra kg

uv run ocr-pipeline kg build -d data/ocr_output
uv run ocr-pipeline kg stats data/ocr_output/kg_graph.json

# retrieval
uv run ocr-pipeline kg query data/ocr_output/kg_graph.json --entity BRCA1
uv run ocr-pipeline kg query data/ocr_output/kg_graph.json --entity BRCA1 --expand
uv run ocr-pipeline kg query data/ocr_output/kg_graph.json --citation 10.1038/nature12345

# export
uv run ocr-pipeline kg export data/ocr_output/kg_graph.json -f graphml
uv run ocr-pipeline kg export data/ocr_output/kg_graph.json -f neo4j

Anomaly detection flags low-confidence documents, empty chunks, and entity hubs. Neo4j export needs uv sync --extra neo4j plus NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD env vars (or --uri/--user/--password).

Docker

The image ships the Tesseract-only core. It reads /data/screenshots and writes /data/output:

docker build -t ocr-pipeline .
docker run -v ~/Pictures:/data/screenshots -v "$PWD/ocr-output:/data/output" ocr-pipeline run
docker run -v ~/Pictures:/data/screenshots -v "$PWD/ocr-output:/data/output" ocr-pipeline watch

# with ML extras:
docker build --build-arg EXTRAS="--extra full" -t ocr-pipeline:full .

Config

ocr-pipeline init writes a minimal config. Lookup order: $OCR_PIPELINE_CONFIG → ./config.yaml → per-user config dir.

Every knob, for reference:

input:
  paths: ["~/Pictures", "/mnt/storage3/aman/screenshots"]
  patterns: ["SCR-*.png", "*.jpg", "*.jpeg", "*.tiff"]
  recursive: true
  exclude_patterns: ["*.photoslibrary/*"]

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
  write_consolidated: true
  consolidated_filename: "all_ocr.md"

watch:
  enabled: true
  debounce_seconds: 5
  db_path: "./data/processed_files.db"

Output example

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:PMID:12345678"]
chunk_index: 0
total_chunks: 2
---

# Screenshot: SCR-20250115-gel.png

## Extracted Text

**Western Blot Analysis of BRCA1 Expression**

Lane 1: WT control
Lane 2: BRCA1 KO (CRISPR)
...

Files land in <output>/<date>/run_NNN/. Chunk files are kept for RAG indexing. output.write_consolidated: true also writes all_ocr.md with one frontmatter block and all source text grouped by image.

Models and dependencies

Weights download on first use, cached in ~/.cache/ocr_pipeline/:

• PaddleOCR models (~200MB)
• scispaCy en_core_sci_lg (~800MB)
• Table Transformer (~500MB)
• LayoutParser PubLayNet (~300MB)

Notes per capability:

• PaddleOCR: pinned to the legacy 2.x API. Falls back to Tesseract when it can't initialize; the fallback is logged and recorded in frontmatter.
• Scientific NER: without scispaCy + en_core_sci_lg, a conservative regex extractor runs instead and the backend is marked accordingly.
• Figures: needs a Detectron2 build matching your Torch/Python. No universal wheel exists, so it's never installed automatically.
• Tables: downloaded by Transformers on first use; needs timm.

ocr-pipeline doctor checks all of the above and prints remediation hints.

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.