Abstract
Tables in documents are a rich source of information, but not yet well-utilised computationally because of the difficulty of extracting their structure and data automatically. In this paper, we progress the state-of-the-art in automatic table extraction by identifying common patterns in table headers to develop rules and heuristics for determining table structure. We describe and evaluate a table understanding system using these patterns and rules.
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Notes
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XSLT, http://www.w3.org/TR/xslt.
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We are grateful to the authors of work for sharing the dataset.
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The ICDAR dataset only has ground truth for table extraction (locating and segmenting).
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Rastan, R., Paik, Hy., Shepherd, J., Haller, A. (2016). Automated Table Understanding Using Stub Patterns. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_33
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