TSRFormer: Table Structure Recognition with Transformers

• URL: http://arxiv.org/abs/2208.04921v1
• Date: Tue, 9 Aug 2022 17:36:13 GMT
• Title: TSRFormer: Table Structure Recognition with Transformers
• Authors: Weihong Lin, Zheng Sun, Chixiang Ma, Mingze Li, Jiawei Wang, Lei Sun, Qiang Huo
• Abstract summary: We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognize the structures of complex tables with geometrical distortions from various table images. We propose a new two-stage DETR based separator prediction approach, dubbed textbfSeparator textbfREgression textbfTRansformer (SepRETR) We achieve state-of-the-art performance on several benchmark datasets, including SciTSR, PubTabNet and WTW.
• Score: 15.708108572696064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various table images. Unlike previous methods, we formulate table separation line prediction as a line regression problem instead of an image segmentation problem and propose a new two-stage DETR based separator prediction approach, dubbed \textbf{Sep}arator \textbf{RE}gression \textbf{TR}ansformer (SepRETR), to predict separation lines from table images directly. To make the two-stage DETR framework work efficiently and effectively for the separation line prediction task, we propose two improvements: 1) A prior-enhanced matching strategy to solve the slow convergence issue of DETR; 2) A new cross attention module to sample features from a high-resolution convolutional feature map directly so that high localization accuracy is achieved with low computational cost. After separation line prediction, a simple relation network based cell merging module is used to recover spanning cells. With these new techniques, our TSRFormer achieves state-of-the-art performance on several benchmark datasets, including SciTSR, PubTabNet and WTW. Furthermore, we have validated the robustness of our approach to tables with complex structures, borderless cells, large blank spaces, empty or spanning cells as well as distorted or even curved shapes on a more challenging real-world in-house dataset.

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