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Unsupervised Segmentation of Text Fragments in Real Scenes

  • Leonardo M. B. Claudino
  • Antônio de P. Braga
  • Arnaldo de A. Araújo
  • André F. Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

This paper proposes a method that aims to reduce a real scene to a set of regions that contain text fragments and keep small number of false positives. Text is modeled and characterized as a texture pattern, by employing the QMF wavelet decomposition as a texture feature extractor. Processing includes segmentation and spatial selection of regions and then content-based selection of fragments. Unlike many previous works, text fragments in different scales and resolutions laid against complex backgrounds are segmented without supervision. Tested in four image databases, the method is able to reduce visual noise to 4.69% and reaches 96.5% of coherency between the localized fragments and those generated by manual segmentation.

Keywords

Manual Segmentation Decomposition Level License Plate Real Scene Visual Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Leonardo M. B. Claudino
    • 1
  • Antônio de P. Braga
    • 1
  • Arnaldo de A. Araújo
    • 2
  • André F. Oliveira
    • 2
  1. 1.Centro de Pesquisa e Desenvolvimento em Engenharia ElétricaUniversidade Federal de Minas GeraisBelo Horizonte, Minas GeraisBrazil
  2. 2.Depto. de Ciência da ComputaçãoUniversidade Federal de Minas GeraisBelo Horizonte, Minas GeraisBrazil

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