ocrbase

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

Features

• Best-in-class OCR - PaddleOCR-VL-1.5 0.9B for accurate text extraction
• Structured extraction - Define schemas, get JSON back
• Built for scale - Queue-based scaling using BullMQ
• Real-time updates - WebSocket notifications for job progress
• Self-hostable - Run on your own infrastructure using Self-Hosting Guide

SDK

NOTE: TS SDK is currently moving to ocrbase-typescript

API Docs

• OpenAPI UI: https://api.ocrbase.dev/openapi
• OpenAPI JSON: https://api.ocrbase.dev/openapi/json

API Usage

# Parse a document
curl -X POST https://api.ocrbase.dev/v1/parse \
  -H "Authorization: Bearer sk_xxx" \
  -F "file=@document.pdf"

# Extract with schema
curl -X POST https://api.ocrbase.dev/v1/extract \
  -H "Authorization: Bearer sk_xxx" \
  -F "file=@invoice.pdf" \
  -F "schemaId=inv_schema_123"

NOTE: Jobs are processed asynchronously.

Realtime Updates

# Subscribe to job status updates
wscat -c "wss://api.ocrbase.dev/v1/realtime?job_id=job_xxx" \
  -H "Authorization: Bearer sk_xxx"

Health Checks

• GET /v1/health/live
• GET /v1/health/ready

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