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Chandra OCR 2 is Datalab's optical character recognition model that converts documents to markdown, HTML, or JSON while preserving layout. It scores 85.8% on the olmOCR benchmark and supports 90+ languages with strong performance on tables, math, handwriting, and complex layouts.
- Chandra 2 achieves 85.8% on olmOCR benchmark (state-of-art), a 12% improvement over Chandra 1's 77.8% multilingual score, with particular strength in math, tables, and complex layouts
- Available through multiple deployment methods: HuggingFace Transformers, vLLM, SGLang, and Docker, with quickstart CLI tools for local use
- Handles 90+ languages, extracts images with captions, reconstructs forms accurately including checkboxes, and supports handwriting recognition across diverse document types
Datalab’s 4 billion-parameter Chandra OCR 2 outperforms GPT-4o and Gemini across independent and multilingual benchmarks, handling complex layouts, math notation, flowcharts and 90 languages with state-of-the-art accuracy. It’s available under Apache 2.0 code with a modified OpenRAIL-M license for weights, runs locally via HuggingFace or vLLM, and doubles throughput over its predecessor.
- Chandra OCR 2 (4B params, open-weight) scored 85.9% on olmOCR vs GPT-4o's 69.9%, and beat Gemini/GPT-5 Mini on multilingual benchmarks, with huge gains on South Asian scripts (Kannada +42.6, Malayalam +46.2, Telugu +39.1).
- It processes full pages in one pass rather than splitting into blocks, giving it an edge on tables, nested headers, checkboxes, handwritten math, and flowcharts exported as Mermaid diagrams.
- Despite shrinking from 9B to 4B parameters, throughput doubled to ~2 pages/sec on an H100 while accuracy improved.
- Code is Apache 2.0 and installable via pip/Docker, but weights use a modified OpenRAIL-M license requiring a paid commercial license for larger companies.