Abstract
This paper proposes a Segmentation and Key point Collaboration Network (SKCN) for structure recognition of complex tables with geometric deformations. First, we combine the cell regions of the segmentation branch and the corner locations of the key point regression branch in the SKCN to obtain more reliable detection bounding box candidates. Then, we propose a Centroid Filtering-based Non-Maximum Suppression algorithm (CF-NMS) to deal with the problem of overlapping detected bounding boxes. After obtaining the bounding boxes of all cells, we propose a post-processing method to predict the logical relationships of cells to finally recover the structure of the table. In addition, we design a module for online generation of tabular data by applying color, shading and geometric transformation to enrich the sample diversity of the existing natural scene table datasets. Experimental results show that our method achieves state-of-the-art performance on two public benchmarks, TAL_OCR_TABLE and WTW.
Z. Li and F. Peng—Authors contributed equally as first author.
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Acknowledgments
This research is supported in part by GD-NSF (No. 2021A1515011870), NSFC (Grant no. 61771199), Zhuhai Industry Core and Key Technology Research Project (No. 2220004002350), and the Science and Technology Foundation of Guangzhou Huangpu Development District (Grant 2020GH17).
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Li, Z., Peng, F., Xue, Y., Hao, N., Jin, L. (2023). Scene Table Structure Recognition with Segmentation and Key Point Collaboration. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14188. Springer, Cham. https://doi.org/10.1007/978-3-031-41679-8_17
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