opendatalab/OmniDocBench resource

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1999 stars · 193 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OmniDocBench is a comprehensive benchmark dataset and evaluation toolkit for document parsing, containing 1,651 annotated PDF pages across 10 document types, 5 layouts, and 5 languages. It includes rich block- and span-level annotations (text, tables, formulas, reading order) plus end-to-end and module-level evaluation code with metrics like TEDS, BLEU, and mAP.

Use cases

• evaluate pdf document parsing models
• benchmark ocr accuracy on real-world documents
• compare table recognition and formula recognition tools
• measure layout detection quality on annotated pdf pages
• test end-to-end document-to-markdown conversion quality

When to choose

• you need a high-quality annotated dataset to evaluate document parsing systems
• you want standardized metrics (TEDS, BLEU, METEOR, mAP) for comparing parsers
• you need coverage of diverse document types like papers, reports, newspapers, and handwriting

When to avoid

• you need a production document parser rather than an evaluation benchmark
• your documents fall outside its covered types, layouts, or languages
• you need training data rather than evaluation data

Facets

dataset · maturity active

benchmarking ocr pdf computer-vision pdf machine-learning python cross-platform document-parsing pdf-parsing benchmark-dataset layout-detection table-recognition formula-recognition cvpr-2025 evaluation natural-language-processing datasets

Member repositories

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markdown · JSON · MCP: product_card(name="opendatalab/OmniDocBench")