Text detection in color scene images based on unsupervised clustering of multi-channel wavelet features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Texts in natural scenes provide us with much useful information. In order to use such information automatically, it is necessary to make computers detect text regions in the images. Gllavata et al. proposed a method based on unsupervised classification of high frequency wavelet coefficients for text detection in video frames [1]. Although the method is very accurate, it does not work so well with some color images, since it lacks the ability of discriminating color difference. This paper proposes an enhanced version of the method. We develop a new unsupervised clustering technique for the classification of multi-channel wavelet features to deal with color images. Experimental results show that the new method yields better results for color scene images.

Original languageEnglish
Title of host publicationProceedings of the Eighth International Conference on Document Analysis and Recognition
Pages690-694
Number of pages5
DOIs
Publication statusPublished - 2005 Dec 1
Event8th International Conference on Document Analysis and Recognition - Seoul, Korea, Republic of
Duration: 2005 Aug 312005 Sep 1

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2005
ISSN (Print)1520-5363

Other

Other8th International Conference on Document Analysis and Recognition
CountryKorea, Republic of
CitySeoul
Period05/8/3105/9/1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Saoi, T., Goto, H., & Kobayashi, H. (2005). Text detection in color scene images based on unsupervised clustering of multi-channel wavelet features. In Proceedings of the Eighth International Conference on Document Analysis and Recognition (pp. 690-694). [1575633] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2005). https://doi.org/10.1109/ICDAR.2005.227