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0.7B MonkeyOCRv2 Outperforms Larger Models on 17-Language Document Parsing
MonkeyOCRv2-B-Parsing reaches 83.3 on MDPBench, a multilingual benchmark covering digital-born and photographed documents across 17 languages.
Results among evaluated open-source models:
• MonkeyOCRv2-B, 0.7B: 83.3
• dots.mocr, 3B: 80.5
• HunyuanOCR-1.5, 1B: 76.8
• PaddleOCR-VL-1.6, 0.9B: 75.0
• MinerU2.5-Pro, 1.2B: 71.0
The central idea is simple: before asking an LLM to reason over a document, the vision encoder must preserve every character stroke, digit, punctuation mark, and layout cue.
MonkeyOCRv2 is pretrained on 113M document images across 17 languages using joint image-to-text generation and pixel-level reconstruction.
Models:
https://huggingface.co/collections/zenosai/monkeyocrv2
Paper:
MonkeyOCRv2: A Visual-Text Foundation Model for Document AI (2607.11562)
GitHub:
https://github.com/Yuliang-Liu/MonkeyOCRv2
Code and model weights are available under Apache-2.0.
We welcome tests on difficult multilingual, photographed, and visually ambiguous documents—especially failure cases.
MonkeyOCRv2-B-Parsing reaches 83.3 on MDPBench, a multilingual benchmark covering digital-born and photographed documents across 17 languages.
Results among evaluated open-source models:
• MonkeyOCRv2-B, 0.7B: 83.3
• dots.mocr, 3B: 80.5
• HunyuanOCR-1.5, 1B: 76.8
• PaddleOCR-VL-1.6, 0.9B: 75.0
• MinerU2.5-Pro, 1.2B: 71.0
The central idea is simple: before asking an LLM to reason over a document, the vision encoder must preserve every character stroke, digit, punctuation mark, and layout cue.
MonkeyOCRv2 is pretrained on 113M document images across 17 languages using joint image-to-text generation and pixel-level reconstruction.
Models:
https://huggingface.co/collections/zenosai/monkeyocrv2
Paper:
MonkeyOCRv2: A Visual-Text Foundation Model for Document AI (2607.11562)
GitHub:
https://github.com/Yuliang-Liu/MonkeyOCRv2
Code and model weights are available under Apache-2.0.
We welcome tests on difficult multilingual, photographed, and visually ambiguous documents—especially failure cases.
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