DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
  Synthetic Data and Global-to-Local Adaptive Perception
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.
671072e2b4ee492058f68981
Zhiyuan Zhao
671072e2b4ee492058f68982
Hengrui Kang
claimed_verified
2025-03-20T10:55:20.096Z
66c598b536a75deef8bb21c0
/avatars/b3721202d01d3c947c36f0beae43ea1c.svg
Hengrui Kang
user
khr0516
671072e2b4ee492058f68983
Bin Wang
claimed_verified
2024-10-18T08:25:12.052Z
63ae9ff5557befe297a76f90
https://cdn-avatars.huggingface.co/v1/production/uploads/1672388558183-noauth.jpeg
Bin Wang
user
wanderkid
671072e2b4ee492058f68984
Conghui He
DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
  Synthetic Data and Global-to-Local Adaptive Perception
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.
DocLayout-YOLO improves document layout analysis speed and accuracy using mesh-candidate bestfit pre-training and global-to-local controllable receptive modules.
multimodal methods unimodal methods DocLayout-YOLO Mesh-candidate BestFit two-dimensional bin packing problem DocSynth-300K Global-to-Local Controllable Receptive Module DocStructBench