DocBank: A Benchmark Dataset for Document Layout Analysis

TL;DR

DocBank is a large, publicly available dataset with fine-grained token-level annotations for document layout analysis, enabling fair comparison of models and advancing multi-modal approaches.

Contribution

It introduces a new benchmark dataset, DocBank, constructed from LaTeX documents with weak supervision, facilitating improved model evaluation and development in document layout analysis.

Findings

Models trained on DocBank accurately recognize document layouts.

The dataset enables fair comparison across different modalities.

Baseline models show promising results on layout recognition.

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 \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 \url{https://github.com/doc-analysis/DocBank}.