MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
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arXiv
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2025
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| author | Niu, Junbo Liu, Zheng Gu, Zhuangcheng Wang, Bin Ouyang, Linke Zhao, Zhiyuan Chu, Tao He, Tianyao Wu, Fan Zhang, Qintong Jin, Zhenjiang Liang, Guang Zhang, Rui Zhang, Wenzheng Qu, Yuan Ren, Zhifei Sun, Yuefeng Zheng, Yuanhong Ma, Dongsheng Tang, Zirui Niu, Boyu Miao, Ziyang Dong, Hejun Qian, Siyi Zhang, Junyuan Chen, Jingzhou Wang, Fangdong Zhao, Xiaomeng Wei, Liqun Li, Wei Wang, Shasha Xu, Ruiliang Cao, Yuanyuan Chen, Lu Wu, Qianqian Gu, Huaiyu Lu, Lindong Wang, Keming Lin, Dechen Shen, Guanlin Zhou, Xuanhe Zhang, Linfeng Zang, Yuhang Dong, Xiaoyi Wang, Jiaqi Zhang, Bo Bai, Lei Chu, Pei Li, Weijia Wu, Jiang Wu, Lijun Li, Zhenxiang Wang, Guangyu Tu, Zhongying Xu, Chao Chen, Kai Qiao, Yu Zhou, Bowen Lin, Dahua Zhang, Wentao He, Conghui |
| author_facet | Niu, Junbo Liu, Zheng Gu, Zhuangcheng Wang, Bin Ouyang, Linke Zhao, Zhiyuan Chu, Tao He, Tianyao Wu, Fan Zhang, Qintong Jin, Zhenjiang Liang, Guang Zhang, Rui Zhang, Wenzheng Qu, Yuan Ren, Zhifei Sun, Yuefeng Zheng, Yuanhong Ma, Dongsheng Tang, Zirui Niu, Boyu Miao, Ziyang Dong, Hejun Qian, Siyi Zhang, Junyuan Chen, Jingzhou Wang, Fangdong Zhao, Xiaomeng Wei, Liqun Li, Wei Wang, Shasha Xu, Ruiliang Cao, Yuanyuan Chen, Lu Wu, Qianqian Gu, Huaiyu Lu, Lindong Wang, Keming Lin, Dechen Shen, Guanlin Zhou, Xuanhe Zhang, Linfeng Zang, Yuhang Dong, Xiaoyi Wang, Jiaqi Zhang, Bo Bai, Lei Chu, Pei Li, Weijia Wu, Jiang Wu, Lijun Li, Zhenxiang Wang, Guangyu Tu, Zhongying Xu, Chao Chen, Kai Qiao, Yu Zhou, Bowen Lin, Dahua Zhang, Wentao He, Conghui |
| contents | We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing Niu, Junbo Liu, Zheng Gu, Zhuangcheng Wang, Bin Ouyang, Linke Zhao, Zhiyuan Chu, Tao He, Tianyao Wu, Fan Zhang, Qintong Jin, Zhenjiang Liang, Guang Zhang, Rui Zhang, Wenzheng Qu, Yuan Ren, Zhifei Sun, Yuefeng Zheng, Yuanhong Ma, Dongsheng Tang, Zirui Niu, Boyu Miao, Ziyang Dong, Hejun Qian, Siyi Zhang, Junyuan Chen, Jingzhou Wang, Fangdong Zhao, Xiaomeng Wei, Liqun Li, Wei Wang, Shasha Xu, Ruiliang Cao, Yuanyuan Chen, Lu Wu, Qianqian Gu, Huaiyu Lu, Lindong Wang, Keming Lin, Dechen Shen, Guanlin Zhou, Xuanhe Zhang, Linfeng Zang, Yuhang Dong, Xiaoyi Wang, Jiaqi Zhang, Bo Bai, Lei Chu, Pei Li, Weijia Wu, Jiang Wu, Lijun Li, Zhenxiang Wang, Guangyu Tu, Zhongying Xu, Chao Chen, Kai Qiao, Yu Zhou, Bowen Lin, Dahua Zhang, Wentao He, Conghui Computer Vision and Pattern Recognition Computation and Language We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead. |
| title | MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2509.22186 |