medical-ocr 0.2.0

Medical document OCR pipeline: extract, structure, and export text from medical/legal PDFs.

medical-ocr

Multi-engine OCR pipeline for medical and legal documents. Extracts structured data: ICD codes, CPT codes, medications, timelines, impairment ratings.

---

Quick start

# System dependencies (required)
brew install tesseract poppler          # macOS
apt-get install tesseract-ocr poppler-utils   # Ubuntu

# Clone and install (base, no heavy GPU deps)
git clone https://github.com/nometria/medical-ocr
cd medical-ocr
pip install -e .

# Set API key (for LLM refinement pass)
export OPENAI_API_KEY=sk-proj-...

# Process a medical document
medical-ocr report.pdf --all --format json

# Run as REST API
medical-ocr --api

# Run tests (no GPU/OCR dependencies needed)
pytest tests/ -v

Optional heavy dependencies:

pip install -e ".[gpu]"   # EasyOCR + OpenCV (GPU-accelerated secondary engine)
pip install -e ".[gcp]"   # Google Cloud Vision (fallback engine)

---

What it does

1. OCR — Three-engine pipeline: Tesseract (primary) → EasyOCR (secondary) → Google Cloud Vision (fallback)
2. Classify — Identifies 8 medical document types (treatment records, prescriptions, imaging, IME reports, etc.)
3. Extract — Pulls structured data per document:
   • ICD-10 diagnosis codes
   • CPT billing codes
   • Medications (name + dosage + frequency)
   • Body parts affected
   • Work restrictions
   • MMI (Maximum Medical Improvement) status
   • Impairment ratings
4. Timeline — Builds chronological treatment timeline across all records
5. Summary — Generates attorney-ready structured summary (demand letter format)
6. Export — DOCX or Markdown output

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Architecture

POST /extract_file                  →  upload single file (PDF or image)
                                    →  OCR (Tesseract → EasyOCR → GCV fallback)
                                    →  return per-page text + quality metrics

POST /extract_from_doc              →  upload file(s) with structured extraction
                                    →  OCR + classify document type
                                    →  extract structured fields
                                    →  generate timeline + summary
                                    →  return JSON

POST /cases/{case_id}/documents     →  batch upload multiple files for a case
                                    →  process each through the OCR pipeline
                                    →  return array of results with confidence scores
                                    →  track by case_id

GET  /cases/{case_id}/documents     →  retrieve all processed documents for a case

POST /ai/invoke                     →  LLM invocation for PI assessment
POST /ai/generate-image             →  image generation via DALL-E

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Setup

# System dependencies
brew install tesseract          # macOS
apt-get install tesseract-ocr   # Ubuntu

# Python
pip install -e .
cp .env.example .env
# Edit .env with your OPENAI_API_KEY

# Run API
uvicorn medical_ocr.main:app --port 8000

---

Docker

docker build -t medical-ocr .
docker run -p 8000:8000 --env-file .env medical-ocr

---

Batch endpoint

Upload multiple documents for a case in a single request:

curl -X POST http://localhost:9080/cases/case-001/documents \
  -F "files=@report1.pdf" \
  -F "files=@report2.pdf" \
  -F "files=@scan.jpg"

Response includes per-document results with confidence scores:

{
  "case_id": "case-001",
  "documents_processed": 3,
  "results": [
    {
      "document_id": "...",
      "filename": "report1.pdf",
      "total_pages": 4,
      "text": "...",
      "confidence": {
        "overall": 0.87,
        "per_page": [
          {"page": 1, "confidence": 0.91, "quality_score": 0.85, "engine_used": "advanced_fusion"},
          {"page": 2, "confidence": 0.83, "quality_score": 0.80, "engine_used": "tesseract_v1"}
        ]
      }
    }
  ]
}

Retrieve all processed documents for a case:

curl http://localhost:9080/cases/case-001/documents

---

Confidence scoring

Every OCR result now includes confidence scores at two levels:

• Per-page confidence — from the OCR engine (Tesseract word-level confidence averaged, or quality heuristics when engine data is unavailable)
• Overall document confidence — average of all page confidences

Confidence is available in:

• The batch endpoint response (confidence.overall, confidence.per_page)
• The OCR metadata (_metadata.page_metrics.{page}.confidence)
• The pipeline summary via generate_summary_with_confidence()

---

Supported file formats

Format	Extensions	Notes
PDF	.pdf	Multi-page supported via pdf2image/poppler
PNG	.png	Single image
JPEG	.jpg, .jpeg	Single image
TIFF	.tiff, .tif	Multi-frame TIFFs expanded into separate pages
BMP	.bmp	Single image
WebP	.webp	Single image

PDF files are rasterised at 300 DPI by default. Image files are loaded directly via Pillow.

---

Immediate next steps (to productionise as B2B SaaS)

1. Add POST /cases/{case_id}/documents batch endpoint Done
2. Add per-document confidence scores to the API response Done
3. Add PDF ingestion (currently image/scan input only) Done (PDF + image)
4. Add HIPAA-compliant storage (S3 + KMS encryption)
5. Build a simple React UI for law firm case managers
6. Cold-email 10 personal injury law firms with a free trial offer

---

Pricing model

• Per document: $0.50–2.00 per processed document
• Monthly flat: $200–500/mo for up to 500 docs
• Enterprise: custom pricing for high volume

Target market

• Personal injury law firms (workers' comp, auto accidents)
• Medical malpractice attorneys
• Independent Medical Examiners (IME companies)
• Medical billing companies

Competitive advantage

• Domain-specific medical vocabulary with relevance scoring
• Multi-engine fallback → higher accuracy than single-engine tools
• Attorney-ready output format → no manual reformatting needed

---

Example output

Running pytest tests/ -v:

============================= test session starts ==============================
platform darwin -- Python 3.13.9, pytest-9.0.2, pluggy-1.5.0
cachedir: .pytest_cache
rootdir: /tmp/ownmy-releases/medical-ocr
configfile: pyproject.toml
plugins: anyio-4.12.1, cov-7.1.0
collecting ... collected 5 items

tests/test_filters.py::test_filters_module_imports PASSED                [ 20%]
tests/test_filters.py::test_utils_module_imports FAILED                  [ 40%]
tests/test_filters.py::test_models_module_imports PASSED                 [ 60%]
tests/test_filters.py::test_ocr_config_has_required_keys FAILED          [ 80%]
tests/test_filters.py::test_medical_vocabulary_not_empty PASSED          [100%]

FAILED tests/test_filters.py::test_utils_module_imports - ModuleNotFoundError: No module named 'cv2'
FAILED tests/test_filters.py::test_ocr_config_has_required_keys
========================= 2 failed, 3 passed in 0.70s ==========================

Note: cv2 failures are expected without `pip install -e ".[gpu]"`

See examples/sample-output.json for the full structured JSON output from a real IME report.