Document layout analysis usually relies on computer vision models to
understand documents while ignoring textual information that is vital to
capture. Meanwhile, high quality labeled datasets with both visual and textual
information are still insufficient. In this paper, we present \textbf{DocBank},
a benchmark dataset that contains 500K document pages with fine-grained
token-level annotations for document layout analysis. DocBank is constructed
using a simple yet effective way with weak supervision from the \LaTeX{}
documents available on the arXiv.com. With DocBank, models from different
modalities can be compared fairly and multi-modal approaches will be further
investigated and boost the performance of document layout analysis. We build
several strong baselines and manually split train/dev/test sets for evaluation.
Experiment results show that models trained on DocBank accurately recognize the
layout information for a variety of documents. The DocBank dataset is publicly
available at https://github.com/doc-analysis/DocBank.

本文介绍了 DocBank，一个包含 500K 篇文档页面的基准数据集，其中包含细粒度的令牌级别的注释，用于文档布局分析的多模态方法的性能提高研究。实验结果表明，使用 DocBank 训练出的模型可以准确识别各种不同类型的文档的布局信息。