DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse
  Synthetic Data and Global-to-Local Adaptive Perception
Authors: name: Zhiyuan Zhao, name: Hengrui Kang, name: Bin Wang, name: Conghui He

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.