Parameter-free geometric document layout analysis

Seong Whan Lee, Dae Seok Ryu

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

Automatic transformation of paper documents into electronic documents requires geometric document layout analysis at the first stage. However, variations in character font sizes, text line spacing, and document layout structures have made it difficult to design a general-purpose document layout analysis algorithm for many years. The use of some parameters has therefore been unavoidable in previous methods. In this paper, we propose a parameter-free method for segmenting the document images into maximal homogeneous regions and identifying them as texts, images, tables, and ruling lines. A pyramidal quadtree structure is constructed for multiscale analysis and a periodicity measure is suggested to find a periodical attribute of text regions for page segmentation. To obtain robust page segmentation results, a confirmation procedure using texture analysis is applied to only ambiguous regions. Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures. The proposed method was experimented with the document database from the University of Washington and the MediaTeam Document Database. The results of these tests have shown that the proposed method provides more accurate results than the previous ones.

Original languageEnglish
Pages (from-to)1240-1256
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume23
Issue number11
DOIs
Publication statusPublished - 2001 Nov 1

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Keywords

  • Geometric document layout analysis
  • Multiscale analysis
  • Page segmentation
  • Parameter-free method
  • Periodicity estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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