Hybrid approach to efficient text extraction in complex color images

Keechul Jung, Jung Hyun Han

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Texture-based methods and connected component (CC) methods have been widely used for text localization. However, these two primary methods have their own strength and weakness. This paper proposes a hybrid approach of the two methods for text localization in complex images. An automatically constructed MLP-based texture classifier can increase the recall rates for complex images with much less user intervention and no explicit feature extraction. The CC-based filtering based on the geometry and shape information enhances the precision rates without affecting overall performance. Then, the time-consuming texture analysis for less relevant pixels is avoided by using CAMShift. Our experimentation shows that the proposed hybrid approach leads to not only robust but also efficient text localization.

Original languageEnglish
Pages (from-to)679-699
Number of pages21
JournalPattern Recognition Letters
Volume25
Issue number6
DOIs
Publication statusPublished - 2004 Apr 19

Keywords

  • CAMShift
  • Connected component
  • Content-based image indexing
  • Mean shift
  • Multi-layer perceptron (MLP)
  • Text localization
  • Texture
  • X-Y recursive cut

ASJC Scopus subject areas

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

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