MinerU2.5: A Decoupled Vision-Language Model for Efficient
  High-Resolution Document Parsing
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.
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Junbo Niu
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Zheng Liu
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Zhuangcheng Gu
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Linke Ouyang
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Tianyao He
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Yuefeng Sun
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SunYuefeng
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Xiaomeng Zhao
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Wentao Zhang
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Conghui He
MinerU2.5: A Decoupled Vision-Language Model for Efficient
  High-Resolution Document Parsing
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.
MinerU2.5, a 1.2B-parameter document parsing vision-language model, achieves state-of-the-art recognition accuracy with computational efficiency through a coarse-to-fine parsing strategy.
document parsing vision-language model coarse-to-fine two-stage parsing layout analysis content recognition downsampled images native-resolution crops data engine pretraining fine-tuning state-of-the-art performance computational overhead