DocBank: A Benchmark Dataset for Document Layout Analysis

Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, Ming Zhou


Abstract
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 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.
Anthology ID:
2020.coling-main.82
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
949–960
Language:
URL:
https://aclanthology.org/2020.coling-main.82
DOI:
10.18653/v1/2020.coling-main.82
Bibkey:
Cite (ACL):
Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. 2020. DocBank: A Benchmark Dataset for Document Layout Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 949–960, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
DocBank: A Benchmark Dataset for Document Layout Analysis (Li et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2020.coling-main.82.pdf
Code
 doc-analysis/DocBank +  additional community code
Data
DocBankPubLayNet