Table Header Detection and Classification

Jing Fang, Prasenjit Mitra, Zhi Tang, C. Lee Giles

Research output: Contribution to conferencePaperpeer-review

13 Scopus citations

Abstract

In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of.that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeerX to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.

Original languageEnglish (US)
Pages599-605
Number of pages7
StatePublished - 2012
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: Jul 22 2012Jul 26 2012

Conference

Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012
Country/TerritoryCanada
CityToronto
Period7/22/127/26/12

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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