Overview
Description
Extract text, layout, and tables from any document in 90+ languages with state-of-the-art accuracy. The big picture: Surya turns messy PDFs, scans, and images into clean, structured data — without needing a massive GPU cluster. It handles dense academic papers, handwritten forms, multilingual documents, and complex tables in a single pass. The bottom line: you get production-grade OCR at 5 pages per second on a single GPU, with output you can feed directly into downstream systems.
Technical scorecard
License
Apache-2.0
Commercial use
Yes
OpenAI-compatible API
Yes
REST API
No
Fine-tuning support
Yes
Quantization support
No
Docker available
No
GUI / no-code available
Yes
Telemetry
Unknown
Offline after setup
Yes
Data & Privacy
Does it send data online?
After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.
Does it store history?
Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.
License checks?
Commercial use is marked as allowed or likely allowed by the listed license.
Telemetry?
Unknown
Last verified: Jul 22, 2026. Maintainer verification should be treated as directory guidance, not legal advice.
Deploying Surya in production?
Stop silent telemetry leaks before they trigger an unintended data leak. Get our Airgap Certainty Blueprint for Surya.
Request Privacy and Telemetry AuditSetup & Installation
A developer can usually get this running with standard docs.
# Start with the official project repository
# https://github.com/datalab-to/suryaHardware Requirements
Works Well With