Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Character Extraction Using Wavelet Transform and Fuzzy Clustering

웨이브렛 변환과 퍼지 군집화를 활용한 문자추출

  • 황중원 (숭실대학교 미디어학과) ;
  • 황재호 (한밭대학교 전자공학과)
  • Published : 2007.07.25
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Abstract

In this paper, a novel approach based on wavelet transform is proposed to process the scraped character which is represented on digital image. The basis idea is that the scraped character is described by its textured neighborhood, and it is decomposed into multiresolution features at different levels with its background region. The image is first decomposed into sub bands by applying Daubechies wavelets. Character features are extracted from the low frequency sub-bands by partition, FCM clustering and area-based region process. High frequency ones are activated by applying local energy density over a moving mask. Features are synthesized in order to reconstruct the original image state through inverse wavelet transform Background region is eliminated and character is extracted. The experimental results demonstrate the effectiveness of the proposed method.

웨이브렛 변환에 근거하여 디지털영상으로부터 문자를 처리하는 새로운 접근법을 제시한다. 대상은 각필(刻筆)문자 영상이다. 각필문자에는 형성된 결상에 유사성이 존속하며 배경부분과 함께 서로 다른 준위의 다해상도 특성들로 분해된다는 점을 착안하였다. 우선 Daubechies 웨이브렛을 적용하여 영상을 부대역들로 분해한다. 저주파 부대역은 분할처리와 FCM근거 퍼지 군집분리 및 면적기반 영역처리기법을 적용하여 문자특성을 추출한다. 고주파 부대역들에는 이동창을 설정하고, 이동창의 국부 에너지를 추정하여 고주파 특성들을 활성화한다. 이들 특성들은 조합되어 역웨이브렛 과정을 통해 본래 영상 상태로 복원되고 배경부분이 배제된 문자를 추출한다. 실험 결과는 제안된 기법의 효과를 보이고 있다.

Keywords

References

  1. L. Feng, Y. Y. Tang, and L. H. Yang, 'A wavelet approach to extracting contours of document images,' in Proc. of Fifth Int'l Conf. on Document Analysis and Recognition, pp. 71-74, Sept. 1999
  2. S. Mallat and S. Zhong, 'Characterization of signals from multiscale edges,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 14, no. 7, pp. 710-732, July 1992 https://doi.org/10.1109/34.142909
  3. D. L. Donoho, 'Denoising by soft-hresholding,' IEEE Trans. Information Theory, Vol. 41, no. 3, pp. 613-627, May 1995 https://doi.org/10.1109/18.382009
  4. K. Etemad, D. Doerman, and R. Chellappa, 'Multiscale segmentation of unstructured document pages using soft decision integration,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, no. 1, pp. 92-96, Jan. 1997 https://doi.org/10.1109/34.566817
  5. A. Busch, W. W. Boles, and S. Sridharan, 'Texture for script identification,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 27, no. 11, pp. 1720-1732, Nov. 2005 https://doi.org/10.1109/TPAMI.2005.227
  6. T. L. Chew, R. Cao, and S. Peiyi, 'Restoration of archival documents using a wavelet technique,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, no. 10, pp. 1399-1404 , Oct. 2002 https://doi.org/10.1109/TPAMI.2002.1039211
  7. S. Pittner and S. V. Kamarthi, 'Feature extraction from wavelet coefficients for pattern recognition tasks,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, no. 1, pp. 83-88, Jan. 1999 https://doi.org/10.1109/34.745739
  8. B. Vidakovic, Statistical modeling by wavelets, John Wiley & Sons, New York, 1999
  9. I. Daubechies, 'The wavelet transform, time frequency localization and signal analysis,' IEEE Trans. Information Theory, Vol. 36, no. 5, pp. 961-1005, Sept. 1990 https://doi.org/10.1109/18.57199
  10. I. Daubechies, Ten Lectures on Wavelets. Philadelphia, SIAM Press, 1992
  11. J. C. Dunn, 'A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters', Journal of Cybernetics, Vol. 3, pp. 32-57, 1973 https://doi.org/10.1080/01969727308546046
  12. D. C. Chang and W. R. Wu, 'Image contrast enhancement based on a histogram transformation of local standard deviation,' IEEE Trans. Medical Imaging, Vol. 17, no. 4, pp. 518-531, Aug. 1998 https://doi.org/10.1109/42.730397
  13. D. Zheng, J. Wang and Z. Xiao, 'Image enhancement based on local standard deviation,' Journal of Information & Computational Science, Vol. 2, no. 2, pp. 429-437, 2005
  14. S. H. Jung and N. C. Kim, 'Adaptive image restoration of sigma filter using local statistics and human visual characteristics,' Electronics Letters, Vol. 24, no. 4, pp. 201-202, Feb. 1988 https://doi.org/10.1049/el:19880134
  15. J. Y. Kim, L. S. Kim, and S. H. Hwang, 'An advanced contrast enhancement using partially overlapped sub-block histogram equalization,' IEEE Trans. Circuits and System Video Technology, Vol. 11, no. 4, pp. 475-484, April, 2001 https://doi.org/10.1109/76.915354
  16. J. C. Bezdek, Pattern recognition with fuzzy objective function algoritms, Plenum Press, New York, 1981
  17. 황재호, '변형된 면적기반영역선별 기법에 의한 문 자영상분할', 전자공학회논문지, 제43권 SP편, 제5 호, 30-36쪽, 2006년 9월
  18. N. Otsu, 'A threshold selection method from gray-level histograms,' IEEE Trans. Systems, Man, and Cybernetics, Vol. 9, no. 1, pp. 62-66, 1979 https://doi.org/10.1109/TSMC.1979.4310076
  19. R. Plamondon and S. N. Srihari, 'Online and off-line handwriting recognition: a comprehensive survey,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, no. 1, pp. 63-84, Jan. 2000 https://doi.org/10.1109/34.824821
  20. Y. Solihin and C. G. Leedham, 'Interal ratio: A new class of global thresholding techniques for handwriting images,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, no. 8, pp. 761-768, August 1999 https://doi.org/10.1109/34.784289
  21. Xiaoyi Jiang, D. Mojon, 'Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, no. 1, pp. 131-137, Jan., 2003 https://doi.org/10.1109/TPAMI.2003.1159954
  22. J. Besag, 'On the statistical analysis of dirty pictures,' J. R. Statist. Soc., Vol. 48, no. 3, pp. 259-302, 1986
  23. A. Owen, 'Image segmentation via iterated conditional expectations,' Technical Report, Department of Statistics, University of Chicago, 1989
  24. H. Zhang, 'Image restoration: Flexible neighborhood systems and iterated conditional expectations,' Statistica Sinica Vol. 3, pp. 117-139, 1993
  25. S. C. Pei and C. N. Lin, 'Image normalization for pattern recognition,' Image Vision Comput., Vol. 13, no. 10, pp. 711-723, Dec. 1995 https://doi.org/10.1016/0262-8856(95)98753-G