Multimodal Tree Decoder for Table of Contents Extraction in Document Images

• URL: http://arxiv.org/abs/2212.02896v1
• Date: Tue, 6 Dec 2022 11:38:31 GMT
• Title: Multimodal Tree Decoder for Table of Contents Extraction in Document Images
• Authors: Pengfei Hu, Zhenrong Zhang, Jianshu Zhang, Jun Du, Jiajia Wu
• Abstract summary: Table of contents (ToC) extraction aims to extract headings of different levels in documents to better understand the outline of the contents. We first introduce a standard dataset, HierDoc, including image samples from 650 documents of scientific papers with their content labels. We propose a novel end-to-end model by using the multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc.
• Score: 32.46909366312659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Table of contents (ToC) extraction aims to extract headings of different levels in documents to better understand the outline of the contents, which can be widely used for document understanding and information retrieval. Existing works often use hand-crafted features and predefined rule-based functions to detect headings and resolve the hierarchical relationship between headings. Both the benchmark and research based on deep learning are still limited. Accordingly, in this paper, we first introduce a standard dataset, HierDoc, including image samples from 650 documents of scientific papers with their content labels. Then we propose a novel end-to-end model by using the multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc. The MTD model is mainly composed of three parts, namely encoder, classifier, and decoder. The encoder fuses the multimodality features of vision, text, and layout information for each entity of the document. Then the classifier recognizes and selects the heading entities. Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed. To evaluate the performance, both the metric of tree-edit-distance similarity (TEDS) and F1-Measure are adopted. Finally, our MTD approach achieves an average TEDS of 87.2% and an average F1-Measure of 88.1% on the test set of HierDoc. The code and dataset will be released at: https://github.com/Pengfei-Hu/MTD.

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