Table structure understanding and its performance evaluation

Yalin Wang, Ihsin T. Phillips, Robert M. Haralick

Research output: Contribution to journalArticle

36 Scopus citations

Abstract

This paper presents a table structure understanding algorithm designed using optimization methods. The algorithm is probability based, where the probabilities are estimated from geometric measurements made on the various entities in a large training set. The methodology includes a global parameter optimization scheme, a novel automatic table ground truth generation system and a table structure understanding performance evaluation protocol. With a document data set having 518 table and 10,934 cell entities, it performed at the 96.76% accuracy rate on the cell level and 98.32% accuracy rate on the table level.

Original languageEnglish (US)
Pages (from-to)1479-1497
Number of pages19
JournalPattern Recognition
Volume37
Issue number7
DOIs
StatePublished - Jul 1 2004
Externally publishedYes

Keywords

  • Document image analysis
  • Document layout analysis
  • Non-parametric statistical modeling
  • Optimization
  • Pattern recognition
  • Performance evaluation
  • Table structure understanding

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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