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chandra

Datalab's SOTA open OCR model: images/PDFs to structured HTML/Markdown/JSON with layout, tables, forms, checkboxes, handwriting and math, in 90+ languages. Local HF or vLLM inference.

12,502 1,258 Python Apache-2.0updated 3 months ago
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Curator's take

Currently the strongest open OCR weights on the olmocr benchmark (85.8, above olmOCR 2 and dots.ocr), with handwriting, filled forms and checkboxes as the real differentiators — plus a serious self-built 90-language benchmark where it averages 72.7% vs Gemini 2.5 Flash's 60.8%. `pip install chandra-ocr`, `chandra_vllm`, done; ~2 pages/s real-world on an H100. The catch is licensing: code is Apache-2.0 but the WEIGHTS are OpenRAIL-M — free for research, personal use and sub-$2M startups, commercial self-hosting needs a Datalab license, and their paid API deliberately stays ahead of the open weights. Pick olmOCR for a fully permissive stack, MinerU when you want a whole parsing pipeline rather than the model itself.

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README.md2 min read

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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 runs an improved Chandra with higher accuracy than the open weights, zero data retention by default, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that has processed 200M+ pages per week — we manage the infrastructure so your workloads finish on time.

Get started with $5 in free credits — 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:

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