ocrbase

Turn PDFs into structured data at scale. Powered by frontier open-weight OCR models.

Features

• Best-in-class OCR - PaddleOCR-VL-0.9B for accurate text extraction
• Structured extraction - Define schemas, get JSON back
• Built for scale - Queue-based processing for thousands of documents
• Real-time updates - WebSocket notifications for job progress
• Self-hostable - Run on your own infrastructure

API Usage

# Parse a document
curl -X POST https://api.ocrbase.dev/api/parse \
  -H "Authorization: Bearer sk_xxx" \
  -F "file=@document.pdf"

# Extract with schema
curl -X POST https://api.ocrbase.dev/api/extract \
  -H "Authorization: Bearer sk_xxx" \
  -F "file=@invoice.pdf" \
  -F "schemaId=inv_schema_123"

Important: Jobs are processed asynchronously. Poll the job status or use WebSocket for real-time updates.

LLM Integration

Best practice: Parse documents with ocrbase before sending to LLMs. Raw PDF binary wastes tokens and produces poor results.

Self-Hosting

See Self-Hosting Guide for deployment instructions.

Requirements: Docker, Bun

Architecture

License

MIT - See LICENSE for details.

Contact

For API access, on-premise deployment, or questions: adammajcher20@gmail.com