title: MinerU: An Open-Source Solution for Precise Document Content Extraction
summary: Document content analysis has been a crucial research area in computer
  vision. Despite significant advancements in methods such as OCR, layout
  detection, and formula recognition, existing open-source solutions struggle to
  consistently deliver high-quality content extraction due to the diversity in
  document types and content. To address these challenges, we present MinerU, an
  open-source solution for high-precision document content extraction. MinerU
  leverages the sophisticated PDF-Extract-Kit models to extract content from
  diverse documents effectively and employs finely-tuned preprocessing and
  postprocessing rules to ensure the accuracy of the final results. Experimental
  results demonstrate that MinerU consistently achieves high performance across
  various document types, significantly enhancing the quality and consistency of
  content extraction. The MinerU open-source project is available at
  https://github.com/opendatalab/MinerU.
paper_authors._id: 66fa495601ab1cdf3672e569
paper_authors.name: Bin Wang
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paper_authors.fullname: Bin Wang
paper_authors.type: user
paper_authors.user: wanderkid
paper_authors._id: 66fa495601ab1cdf3672e56a
paper_authors.name: Chao Xu
paper_authors._id: 66fa495601ab1cdf3672e56b
paper_authors.name: Xiaomeng Zhao
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paper_authors.fullname: xiaomeng zhao
paper_authors.type: user
paper_authors.user: myhloli
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paper_authors.name: Linke Ouyang
paper_authors._id: 66fa495601ab1cdf3672e56d
paper_authors.name: Fan Wu
paper_authors._id: 66fa495601ab1cdf3672e56e
paper_authors.name: Zhiyuan Zhao
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paper_authors.name: Rui Xu
paper_authors._id: 66fa495601ab1cdf3672e570
paper_authors.name: Kaiwen Liu
paper_authors._id: 66fa495601ab1cdf3672e571
paper_authors.name: Yuan Qu
paper_authors._id: 66fa495601ab1cdf3672e572
paper_authors.name: Fukai Shang
paper_authors._id: 66fa495601ab1cdf3672e573
paper_authors.name: Bo Zhang
paper_authors.status: claimed_verified
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paper_authors._id: 643dfd235aafbdca3a5792c0
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paper_authors.fullname: Bo Zhang
paper_authors.type: user
paper_authors.user: BoZhang
paper_authors._id: 66fa495601ab1cdf3672e574
paper_authors.name: Liqun Wei
paper_authors._id: 66fa495601ab1cdf3672e575
paper_authors.name: Zhihao Sui
paper_authors._id: 66fa495601ab1cdf3672e576
paper_authors.name: Wei Li
paper_authors._id: 66fa495601ab1cdf3672e577
paper_authors.name: Botian Shi
paper_authors._id: 66fa495601ab1cdf3672e578
paper_authors.name: Yu Qiao
paper_authors._id: 66fa495601ab1cdf3672e579
paper_authors.name: Dahua Lin
paper_authors._id: 66fa495601ab1cdf3672e57a
paper_authors.name: Conghui He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-30T12:33:29.147Z
paper_authors._id: 63f9fca8d4349b157a109eec
paper_authors.avatarUrl: /avatars/fa1f2ae7972d7cde99dab178136ccbb0.svg
paper_authors.fullname: Conghui He
paper_authors.type: user
paper_authors.user: conghui
paper_title: MinerU: An Open-Source Solution for Precise Document Content Extraction
paper_summary: Document content analysis has been a crucial research area in computer
  vision. Despite significant advancements in methods such as OCR, layout
  detection, and formula recognition, existing open-source solutions struggle to
  consistently deliver high-quality content extraction due to the diversity in
  document types and content. To address these challenges, we present MinerU, an
  open-source solution for high-precision document content extraction. MinerU
  leverages the sophisticated PDF-Extract-Kit models to extract content from
  diverse documents effectively and employs finely-tuned preprocessing and
  postprocessing rules to ensure the accuracy of the final results. Experimental
  results demonstrate that MinerU consistently achieves high performance across
  various document types, significantly enhancing the quality and consistency of
  content extraction. The MinerU open-source project is available at
  https://github.com/opendatalab/MinerU.