Identifying table boundaries in digital documents via sparse line detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored in a database, the data can be queried and joined with other data using database management systems. In order to prepare the data source for table search, accurately detecting the table boundary plays a crucial role for the later table structure decomposition. Table boundary detection and content extraction is a challenging problem because tabular formats are not standardized across all documents. In this paper, we propose a simple but effective preprocessing method to improve the table boundary detection performance by considering the sparse-line property of table rows. Our method easily simplifies the table boundary detection problem into the sparse line analysis problem with much less noise. We design eight line label types and apply two machine learning techniques, Conditional Random Field (CRF) and Support Vector Machines (SVM), on the table boundary detection field. The experimental results not only compare the performances between the machine learning methods and the heuristical-based method, but also demonstrate the effectiveness of the sparse line analysis in the table boundary detection.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08
Pages1311-1320
Number of pages10
DOIs
StatePublished - Dec 1 2008
Event17th ACM Conference on Information and Knowledge Management, CIKM'08 - Napa Valley, CA, United States
Duration: Oct 26 2008Oct 30 2008

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other17th ACM Conference on Information and Knowledge Management, CIKM'08
CountryUnited States
CityNapa Valley, CA
Period10/26/0810/30/08

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Liu, Y., Mitra, P., & Giles, C. L. (2008). Identifying table boundaries in digital documents via sparse line detection. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08 (pp. 1311-1320). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1458082.1458255