title: DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
    Synthetic Data and Global-to-Local Adaptive Perception
summary: Document Layout Analysis is crucial for real-world document understanding
  systems, but it encounters a challenging trade-off between speed and accuracy:
  multimodal methods leveraging both text and visual features achieve higher
  accuracy but suffer from significant latency, whereas unimodal methods relying
  solely on visual features offer faster processing speeds at the expense of
  accuracy. To address this dilemma, we introduce DocLayout-YOLO, a novel
  approach that enhances accuracy while maintaining speed advantages through
  document-specific optimizations in both pre-training and model design. For
  robust document pre-training, we introduce the Mesh-candidate BestFit
  algorithm, which frames document synthesis as a two-dimensional bin packing
  problem, generating the large-scale, diverse DocSynth-300K dataset.
  Pre-training on the resulting DocSynth-300K dataset significantly improves
  fine-tuning performance across various document types. In terms of model
  optimization, we propose a Global-to-Local Controllable Receptive Module that
  is capable of better handling multi-scale variations of document elements.
  Furthermore, to validate performance across different document types, we
  introduce a complex and challenging benchmark named DocStructBench. Extensive
  experiments on downstream datasets demonstrate that DocLayout-YOLO excels in
  both speed and accuracy. Code, data, and models are available at
  https://github.com/opendatalab/DocLayout-YOLO.
paper_authors._id: 671072e2b4ee492058f68981
paper_authors.name: Zhiyuan Zhao
paper_authors._id: 671072e2b4ee492058f68982
paper_authors.name: Hengrui Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T10:55:20.096Z
paper_authors._id: 66c598b536a75deef8bb21c0
paper_authors.avatarUrl: /avatars/b3721202d01d3c947c36f0beae43ea1c.svg
paper_authors.fullname: Hengrui Kang
paper_authors.type: user
paper_authors.user: khr0516
paper_authors._id: 671072e2b4ee492058f68983
paper_authors.name: Bin Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T08:25:12.052Z
paper_authors._id: 63ae9ff5557befe297a76f90
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672388558183-noauth.jpeg
paper_authors.fullname: Bin Wang
paper_authors.type: user
paper_authors.user: wanderkid
paper_authors._id: 671072e2b4ee492058f68984
paper_authors.name: Conghui He
paper_title: DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
    Synthetic Data and Global-to-Local Adaptive Perception
paper_summary: Document Layout Analysis is crucial for real-world document understanding
  systems, but it encounters a challenging trade-off between speed and accuracy:
  multimodal methods leveraging both text and visual features achieve higher
  accuracy but suffer from significant latency, whereas unimodal methods relying
  solely on visual features offer faster processing speeds at the expense of
  accuracy. To address this dilemma, we introduce DocLayout-YOLO, a novel
  approach that enhances accuracy while maintaining speed advantages through
  document-specific optimizations in both pre-training and model design. For
  robust document pre-training, we introduce the Mesh-candidate BestFit
  algorithm, which frames document synthesis as a two-dimensional bin packing
  problem, generating the large-scale, diverse DocSynth-300K dataset.
  Pre-training on the resulting DocSynth-300K dataset significantly improves
  fine-tuning performance across various document types. In terms of model
  optimization, we propose a Global-to-Local Controllable Receptive Module that
  is capable of better handling multi-scale variations of document elements.
  Furthermore, to validate performance across different document types, we
  introduce a complex and challenging benchmark named DocStructBench. Extensive
  experiments on downstream datasets demonstrate that DocLayout-YOLO excels in
  both speed and accuracy. Code, data, and models are available at
  https://github.com/opendatalab/DocLayout-YOLO.
paper_ai_summary: DocLayout-YOLO improves document layout analysis speed and accuracy using mesh-candidate bestfit pre-training and global-to-local controllable receptive modules.
paper_ai_keywords: multimodal methods unimodal methods DocLayout-YOLO Mesh-candidate BestFit two-dimensional bin packing problem DocSynth-300K Global-to-Local Controllable Receptive Module DocStructBench