# 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

The default install is small: Tesseract OCR only, no multi-GB ML downloads.

```bash
uv sync                          # install the core pipeline
uv run ocr-pipeline setup        # installs Tesseract if missing (asks first)
uv run ocr-pipeline init         # write a config for this machine
uv run ocr-pipeline demo         # verify everything works on a sample screenshot
uv run ocr-pipeline run          # process your screenshots
```

Config is found in this order: `$OCR_PIPELINE_CONFIG`, `./config.yaml`, then the
per-user config directory (`~/Library/Application Support/ocr-pipeline` on macOS,
`~/.config/ocr-pipeline` on Linux). Running `run` with neither a config nor
`--input-dir` stops with guidance instead of scanning a default directory.

Use `--force` to reprocess files already recorded in the processing database. The
`--exclude` option is repeatable and prevents recursive scans from entering bundles
such as macOS `.photoslibrary` directories.

### Optional capabilities

Heavy ML features are opt-in extras and degrade gracefully when absent (a warning
is logged and that step is skipped):

| Extra | Provides |
|-------|----------|
| `paddle` | PaddleOCR engine (`ocr.engine: paddleocr` or `auto`) |
| `scientific` | spaCy/scispaCy NER (`entities.enabled: true`) |
| `tables` | Table Transformer detection |
| `figures` | LayoutParser figure/caption detection (needs a Detectron2 build) |
| `full` | All of the above |

```bash
uv run ocr-pipeline setup full   # runs: uv sync --extra full + downloads en_core_sci_lg
```

`en_core_sci_lg` is distributed separately from scispaCy. The compatible scispaCy
0.5.4 release requires spaCy 3.7.x; if the model install conflicts with your spaCy
version, use a dedicated Python 3.11 environment as described in
`ocr-pipeline setup full --dry-run` output, and enable `entities` only there.

## kg_ocr (experimental)

The top-level `kg_ocr` package is a prototype txtai/litellm RAG interface over the
pipeline's output. It is not the supported interface (that is the `ocr-pipeline`
CLI) and its dependencies are not installed by default:

```bash
uv sync --extra kg
```

## Docker

The image ships the Tesseract-only core with a ready-made config that reads from
`/data/screenshots` and writes to `/data/output`:

```bash
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

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

## Config

`ocr-pipeline init` writes a minimal working config; the example below shows every
knob for reference.

<details>
<summary><code>config.yaml</code></summary>

```yaml
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"
  frontmatter:
    - source_path
    - source_hash
    - timestamp
    - ocr_engine
    - ocr_confidence_mean
    - language
    - detected_entities
    - entity_extraction_backend
    - 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/`. Individual chunk files are retained for RAG indexing. Set `output.write_consolidated: true` to also write `all_ocr.md` containing one frontmatter block and all source text grouped by image.

## 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

Model weights are only fetched when the matching extra is installed and enabled.
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)

## Runtime requirements and health

The core pipeline can run with Tesseract alone. PaddleOCR, scientific NER, figure detection, and table detection are optional capabilities with heavyweight, platform-specific dependencies. The pipeline records the OCR engine actually used and the entity-extraction backend in generated frontmatter; inspect them after each run rather than assuming configured models loaded.

- **PaddleOCR:** the project pins the legacy 2.x API used by the pipeline. Install the locked environment with `uv sync`; a startup fallback to Tesseract is logged when Paddle cannot initialize.
- **Scientific NER:** install a compatible `scispacy` distribution and the separately distributed `en_core_sci_lg` model before enabling production scientific NER. Without it, the pipeline uses its conservative regex fallback and marks the backend accordingly.
- **Figures:** `layoutparser`'s `Detectron2LayoutModel` requires a Detectron2 build matching your Torch/Python platform. It is intentionally not forced as a universal dependency because no single wheel supports every platform.
- **Tables:** the Table Transformer model is downloaded by Transformers on first use; ensure the selected model's optional dependencies (including `timm`, when required by that model revision) are installed in the runtime image.

Use `ocr-pipeline config` to verify effective settings. A missing optional model is logged and produces empty results for that detector instead of failing a complete batch.

## TODO

- [ ] Build a knowledge graph from extracted entities/citations