Chandra is an OCR model that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. You can try Chandra in the free playground here, or at a hosted API here. - Convert documents to markdown, html, or json with detailed layout information - Good handwriting support - Reconstructs forms accurately, including checkboxes - Good support for tables, math, and complex layouts - Extracts images and diagrams, with captions and structured data - Support for 40+ languages
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View details ↗Chandra 2 is a state of the art OCR model from Datalab that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. Try Chandra in the free playground, or use the hosted API for higher accuracy and speed. - 85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1) - Significant improvements to math, tables, complex layouts - Improved layout, especially on wider documents - Significantly better image captioning - 90+ language support with major accuracy gains
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View details ↗Indra OCR is a 1M-parameter OCR model that runs on CPU. It turns PDFs and images into markdown, text, or JSON with boxes, and it reads handwriting, 80+ languages, and pages at any angle. Indra OCR is built for speed on CPUs. When you need the highest accuracy, on handwriting, math, complex tables and degraded scans, our managed platform runs Chandra, our state of the art OCR model, with 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 can handle 1B+ pages a day.
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View details ↗lift is a structured extraction model from Datalab that pulls structured JSON out of PDFs and images. Pass any JSON schema and lift returns a JSON object matching it, using schema-constrained decoding to guarantee valid, well-typed output. Try lift in the free playground, or use the hosted API for higher accuracy, per-field verification, and citations. - Extract structured data from documents - Pass any JSON schema - Handles multi-page documents in a single pass, including values that span pages - Two inference modes: local (HuggingFace) and remote (vLLM server) - CLI for single files, inline schemas, or whole directories - Schema Studio: a Streamlit app to build, save, and test schemas against your documents
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View details ↗A lightweight document **layout detection** model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. Released under the AI Pubs OpenRAIL-M license (see `LICENSE`) — the same license as the surya-ocr-2 model weights.
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View details ↗- Accuracy - scores 83.3% on olmOCR-bench (top under 3B params) - Speed - throughput of 5 pages/s on an RTX 5090 - Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Table recognition (rows + columns) It works on a range of documents (see usage and benchmarks). Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra.
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View details ↗- Accuracy - scores 83.3% on olmOCR-bench (top under 3B params) - Speed - throughput of 5 pages/s on an RTX 5090 - Multilingual - scores 87.2% on an internal benchmark set of 91 languages (more here) - Layout analysis (table, image, header, etc.) with reading order - Table recognition (rows + columns) It works on a range of documents (see usage and benchmarks). Our managed platform runs both Surya, and variants of our highest accuracy model, Chandra.
Input Text · Image→ Output Text
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