DocBank: A Benchmark Dataset for Document Layout Analysis

Proceedings of the 28th International Conference on Computational Linguistics, pages 949–960 Barcelona, Spain (Online), December 8-13, 2020 949 DocBank: A Benchmark Dataset for Document Layout Analysis Minghao Li1∗, Yiheng Xu2∗, Lei Cui2, Shaohan Huang2, Furu Wei2, Zhoujun Li1, Ming Zhou2 1Beihang University 2Microsoft Research Asia {liminghao1630,lizj}@buaa.edu.cn {v-yixu,lecu,shaohanh,fuwei,mingzhou}@microsoft.com 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 ap- proaches 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. Ex- periment results show that models trained on DocBank accurately recognize the layout infor- mation for a variety of documents. The DocBank dataset is publicly available at https: //github.com/doc-analysis/DocBank. 1 Introduction Document layout analysis is an important task in many document understanding applications as it can transform semi-structured information into a structured representation, meanwhile extracting key infor- mation from the documents. It is a challenging problem due to the varying layouts and formats of the documents. Existing techniques have been proposed based on conventional rule-based or machine learn- ing methods, where most of them fail to generalize well because they rely on hand crafted features that may be not robust to layout variations. Recently, the rapid development of deep learning in computer vision has significantly boosted the data-driven image-based approaches for document layout analysis. Although these approaches have been widely adopted and made significant progress, they usually lever- age visual features while neglecting textual features from the documents. Therefore, it is inevitable to explore how to leverage the visual and textual information in a unified way for document layout analysis. Nowadays, the state-of-the-art computer vision and NLP models are often built upon the pre-trained models (Peters et al., 2018; Radford et al., 2018; Devlin et al., 2018; Lample and Conneau, 2019; Yang et al., 2019; Dong et al., 2019; Raffel et al., 2019; Xu et al., 2019) followed by fine-tuning on specific downstream tasks, which achieves very promising results. However, pre-trained models not only require large-scale unlabeled data for self-supervised learning, but also need high quality labeled data for task-specific fine-tuning to achieve good performance. For document layout analysis tasks, there have been some image-based document layout datasets, while most of them are built for computer vision approaches and they are difficult to apply to NLP methods. In addition, image-based datasets mainly include the page images and the bounding boxes of large semantic structures, which are not fine-grained token-level annotations. Moreover, it is also time-consuming and labor-intensive to produce human- labeled and fine-grained token-level text block …

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