README

Datalab

  State of the Art models for Document Intelligence


  
  
  


  
  
  




 Chandra OCR 2

Chandra OCR 2 is a state of the art OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.

 Try Chandra on Datalab

Our managed platform uses an improved Chandra and other models, with higher accuracy than the open weights, opt-in zero data retention, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that has processed 1B+ pages in a day at peak — we manage the infrastructure so your workloads finish on time.

The Datalab API turns documents into verifiable knowledge your agents act on. Every result traces back to its page and position, and one API can parse, extract, fill and edit documents.

Get started with about 5,000 free convert pages every month (work email) — sign up — takes under 30 seconds — or try Chandra in our public playground.

Commercial self-hosting requires a license — see Commercial usage. For on-prem licensing, contact us.

 News

- 3/2026 - Chandra 2 is here with significant improvements to math, tables, layout, and multilingual OCR
- 10/2025 - Chandra 1 launched

 Features

- Tops external olmocr benchmark and significant improvement in internal multilingual benchmarks
- Convert documents to markdown, html, or json with detailed layout information
- Support for 90+ languages (benchmark below)
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, and adds captions and structured data
- Two inference modes: local (HuggingFace) and remote (vLLM server)



 Quickstart

The easiest way to start is with the CLI tools:

shell
pip install chandra-ocr

 With vLLM (recommended, lightweight install)
chandra_vllm
chandra input.pdf ./output

 With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf

 Interactive streamlit app
pip install chandra-ocr[app]
chandra_app


 Benchmarks

Multilingual performance was a focus for us with Chandra 2.  There isn't a good public multilingual OCR benchmark, so we made our own.  This tests tables, math, ordering, layout, and text accuracy.



See full scores below. We also have a full 90-language benchmark.

We also benchmarked Chandra 2 with the widely accepted olmocr benchmark:



See full scores below.

 Examples

| Type | Name                     | Link                                                                                                        |
|------|--------------------------|-------------------------------------------------------------------------------------------------------------|
| Math | CS229 Textbook           | View                    |
| Math | Handwritten Math         | View         |
| Math | Chinese Math             | View             |
| Tables | Statistical Distribution | View         |
| Table