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YOLO-table: disclosure document table detection with involution

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Abstract

As financial document automation becomes more general, table detection is receiving more and more attention as an important part of document automation. Disclosure documents contain both bordered and borderless tables of varying lengths, and there is currently no model that performs well on these types of documents. To solve this problem, we propose a table detection model based on YOLO-table. We introduce involution into the backbone of the network to improve the network’s ability to learn table spatial layout features and design a simple Feature Pyramid Network to improve model effectiveness. In addition, this paper proposes a table-based augment method. We experiment on a disclosure document dataset, and the results show that the F1-measure of the YOLO-table reaches 97.3%. Compared with YOLOv3, our method improves the accuracy by 2.8% and the speed by 1.25 times. It also evaluates the ICDAR2013 and ICDAR2019 Table Competition datasets and achieves state-of-the-art performance.

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Authors and Affiliations

  1. Shenzhen Securities Information Co., Ltd., Futian District, Shenzhen, China

    Daqian Zhang, Ruibin Mao, Runting Guo, Yang Jiang & Jing Zhu

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  1. Daqian Zhang
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  2. Ruibin Mao
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  3. Runting Guo
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  4. Yang Jiang
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Correspondence to Daqian Zhang.

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Zhang, D., Mao, R., Guo, R. et al. YOLO-table: disclosure document table detection with involution. IJDAR 26, 1–14 (2023). https://doi.org/10.1007/s10032-022-00400-z

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