MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908818734055424 |
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| author | Li, Zhang Liu, Yuliang Liu, Qiang Ma, Zhiyin Zhang, Ziyang Zhang, Shuo Yang, Biao Guo, Zidun Zhang, Jiarui Wang, Xinyu Bai, Xiang |
| author_facet | Li, Zhang Liu, Yuliang Liu, Qiang Ma, Zhiyin Zhang, Ziyang Zhang, Shuo Yang, Biao Guo, Zidun Zhang, Jiarui Wang, Xinyu Bai, Xiang |
| contents | We introduce MonkeyOCR, a document parsing model that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies what would otherwise be a complex multi-tool pipeline and avoids the inefficiencies of processing full pages with giant end-to-end models. In SRR, document parsing is abstracted into three fundamental questions - ``Where is it?'' (structure), ``What is it?'' (recognition), and ``How is it organized?'' (relation) - corresponding to structure detection, content recognition, and relation prediction. To support this paradigm, we present MonkeyDoc, a comprehensive dataset with 4.5 million bilingual instances spanning over ten document types, which addresses the limitations of existing datasets that often focus on a single task, language, or document type. Leveraging the SRR paradigm and MonkeyDoc, we trained a 3B-parameter document foundation model. We further identify parameter redundancy in this model and propose contiguous parameter degradation (CPD), enabling the construction of models from 0.6B to 1.2B parameters that run faster with acceptable performance drop. MonkeyOCR achieves state-of-the-art performance, surpassing previous open-source and closed-source methods, including Gemini 2.5-Pro. Additionally, the model can be efficiently deployed for inference on a single RTX 3090 GPU. Code and models will be released at https://github.com/Yuliang-Liu/MonkeyOCR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm Li, Zhang Liu, Yuliang Liu, Qiang Ma, Zhiyin Zhang, Ziyang Zhang, Shuo Yang, Biao Guo, Zidun Zhang, Jiarui Wang, Xinyu Bai, Xiang Computer Vision and Pattern Recognition We introduce MonkeyOCR, a document parsing model that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies what would otherwise be a complex multi-tool pipeline and avoids the inefficiencies of processing full pages with giant end-to-end models. In SRR, document parsing is abstracted into three fundamental questions - ``Where is it?'' (structure), ``What is it?'' (recognition), and ``How is it organized?'' (relation) - corresponding to structure detection, content recognition, and relation prediction. To support this paradigm, we present MonkeyDoc, a comprehensive dataset with 4.5 million bilingual instances spanning over ten document types, which addresses the limitations of existing datasets that often focus on a single task, language, or document type. Leveraging the SRR paradigm and MonkeyDoc, we trained a 3B-parameter document foundation model. We further identify parameter redundancy in this model and propose contiguous parameter degradation (CPD), enabling the construction of models from 0.6B to 1.2B parameters that run faster with acceptable performance drop. MonkeyOCR achieves state-of-the-art performance, surpassing previous open-source and closed-source methods, including Gemini 2.5-Pro. Additionally, the model can be efficiently deployed for inference on a single RTX 3090 GPU. Code and models will be released at https://github.com/Yuliang-Liu/MonkeyOCR. |
| title | MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05218 |