Tab.IAIS: Flexible Table Recognition and Semantic Interpretation System
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Table extraction is an important but still unsolved problem. In this paper, we introduce a flexible end-to-end table extraction system. We develop two rule-based algorithms that perform the complete table recognition process and support the most frequent table formats found in the scientific literature. Moreover, to incorporate the extraction of semantic information into the table recognition process, we develop a graph-based table interpretation method. We conduct extensive experiments on the challenging table recognition benchmarks ICDAR 2013 and ICDAR 2019. Our table recognition approach achieves results competitive with state-of-the-art approaches. Moreover, our complete information extraction system exhibited a high F1 score of 0.7380 proving the utility of our approach.
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