Abstract
Tables contain rich multi-dimensional information which can be an important source for many data analytics applications. However, table structure information is often unavailable in digitized documents such as PDF or image files, making it hard to perform automatic analysis over high-quality table data. Table structure recognition from digitized files is a non-trivial task, as table layouts often vary greatly in different files. Moreover, the existence of spanning cells further complicates the table structure and brings big challenges in table structure recognition. In this paper, we model the problem as a cell relation extraction task and propose T2, a novel two-phase approach that effectively recognizes table structures from digitized documents. T2 introduces a general concept termed prime relation, which captures the direct relations of cells with high confidence. It further constructs an alignment graph and employs message passing network to discover complex table structures. We validate our approach via extensive experiments over three benchmark datasets. The results demonstrate T2 is highly robust for recognizing complex table structures.
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References
Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Göbel, M., Hassan, T., Oro, E., Orsi, G.: ICDAR 2013 table competition. In: ICDAR, pp. 1449–1453 (2013)
Kieninger, T., Dengel, A.: The T-Recs table recognition and analysis system. In: DAS, pp. 255–270 (1998)
Li, Y., Huang, Z., Yan, J., Zhou, Y., Ye, F., Liu, X.: GFTE: graph-based financial table extraction. In: ICPR Workshops, pp. 644–658 (2020)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: ICML, pp. 807–814 (2010)
Oro, E., Ruffolo, M.: PDF-TREX: an approach for recognizing and extracting tables from PDF documents. In: ICDAR, pp. 906–910 (2009)
Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: CascadeTabNet: an approach for end to end table detection and structure recognition from image-based documents. In: CVPR Workshops, pp. 572–573 (2020)
Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking table recognition using graph neural networks. In: ICDAR, pp. 142–147 (2019)
Shigarov, A., Mikhailov, A., Altaev, A.: Configurable table structure recognition in untagged pdf documents. In: DocEng, pp. 119–122 (2016)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM TOG 38(5), 146:1–146:12 (2019)
Acknowledgement
This work is supported by NSF of China (62072461).
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Li, H., Zeng, L., Zhang, W., Zhang, J., Fan, J., Zhang, M. (2022). A Two-Phase Approach for Recognizing Tables with Complex Structures. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_47
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DOI: https://doi.org/10.1007/978-3-031-00123-9_47
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