Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Text Detection Method Using Wavelet Packet Analysis and Unsupervised Classifier

  • Lee, Geum-Boon (Dept. of Computer Engineering, Graduate School, Chosun University) ;
  • Odoyo Wilfred O. (Dept. of Computer Engineering, Graduate School, Chosun University) ;
  • Kim, Kuk-Se (Dept. of Computer Engineering, Graduate School, Chosun University) ;
  • Cho, Beom-Joon (Dept. of Computer Engineering, Graduate School, Chosun University)
  • Published : 2006.12.30
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Abstract

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

Keywords

References

  1. S. N. Srihari, 'Document Image Understanding,' Pro. IEEE Computer Society, Fall Joint Computer Conf. pp. 87-96, 1986
  2. S. Sural and P. K. Das, 'A two step algorithm and its parallelization for the generation of minimum containing rectangles for document image segmentation,' in Proc. Int. Conf. Document Analysis and Recognition, pp. 173-176, 1999
  3. T. Paviidis and J.Zhou, 'Page segmentation using the description of the background,' Comput. Vis. Image Understanding, Vol. 70, No.3, pp.350-369, 1998 https://doi.org/10.1006/cviu.1998.0691
  4. A. Antaonacopulos, 'Page Segmentation by white streams,' in Proc. 10th. Int. Conf. Pattern Recognition, pp. 945-953, 1991
  5. A. Laine and J. Fan, 'Texture Classification by Wavelet Packet Signatures,' IEEE Tran. on pattern Analysis and machine Intelligence, pp. 1186-1191, 1993
  6. H. Kim and P. Liang, 'Target Extraction from Clutter Images Using Wavelet Packet Analysis,' IEEE National Radar Conference, pp. 195-200, 1998
  7. S. Mallat, 'A Theory for Multiresolution signal decomposition: The Wavelet Representation,' IEEE Trans. Pattern Analysis, Machine Intell., Vol. 11, pp. 674-693, 1989 https://doi.org/10.1109/34.192463
  8. R. Bajcsy, 'Computer description of textured surfaces,' in Proc. 3rd Int. joint Conf. Artificial Intell., Aug., pp. 572-579, 1973
  9. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing 2nd Edition, Prentice Hall, 2002
  10. G. B. Lee and Y. S. Kim, 'A Fuzzy Contrast Enhancement Technique using the improved IAFC model,' Korea Fuzzy Logic and Intelligent Systems Society, Vol. 11, NO.9, pp. 777-781, 2001
  11. Y. S. Kim, 'An Unsupervised Neural Network Using Fuzzy Learning Rule,' IEEE International Fuzzy Systems Conference Proceedings, Vol. 1, pp. 349-343, 1999
  12. M. Acharyya and M. K. Kundu, ' Document Image Segmentation Using Wavelet Scale-Space Features,' IEEE Trans. Circuits and Systems for Video Technology, Vol. 12, No. 12, pp. 1117-1127, 2002 https://doi.org/10.1109/TCSVT.2002.806812