The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Noise Removal using Support Vector Regression in Noisy Document Images

  • Received : 2012.06.18
  • Accepted : 2012.07.23
  • Published : 2012.08.31
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Abstract

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

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References

  1. Chang, C. C. and Lin, C. J. (2011). LIBSVM: A library for support vector machines, URL: www.csie.ntu.edu.tw/cjlin/libsvm/
  2. Cherkassky, V. and Mulier, F. (1998). Learning from Data: Concepts Theory and Methods, Wiley, New York.
  3. Don, H. S. (2001). A noise attribute thresholding method for document image binarization, International Journal on Document Analysis and Recognition, 4, 131-138. https://doi.org/10.1007/s100320100062
  4. Donoho, D. L. (1995). De-noising by soft thresholding, IEEE Transactions on Information Theory, 41, 613-627. https://doi.org/10.1109/18.382009
  5. Donoho, D. L. and Johnstone, I. M. (1994). Spatial adaptation by wavelet shrinkage, Biometrika, 81, 425-455. https://doi.org/10.1093/biomet/81.3.425
  6. Gonzales, R. C. and Woods, R. E. (1992). Digital Image Processing, Addison-Wesley Publishing Company.
  7. Li, A., Mersereau, R. and Simske, S. (2007). Blind image deconvolution through support vector regression, IEEE Transactions on Neural Networks, 931-935.
  8. Li, D. (2009). Support vector regression based image denoising, Image and Vision Computing, 27, 623-627. https://doi.org/10.1016/j.imavis.2008.06.006
  9. Lin, T. C. and Yu, P. T. (2004). Adaptive two-pass median filter based on support vector machines for image restoration, Neural Computation, 16, 192-206.
  10. Liu, H., Sun, F. and Sun, Z. (2006). Image filtering using support vector machine, Lecture Notes in Computer Science, 3972, 533-538.
  11. Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer-Verlag.
  12. Vetterli, M., Chang, S. G. and Yu, B. (2000). Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, 9, 1532-1546. https://doi.org/10.1109/83.862633
  13. Wang, X. Y. and Fu, Z. K. (2010). A wavelet-based image denoising using least squares support vector machine, Engineering Applications of Artificial Intelligence, 862-871.
  14. Wang, X. Y., Yang, H. Y. and Fu, Z. K. (2010). A new wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine, Expect Systems with Applications, 37, 7040-7049. https://doi.org/10.1016/j.eswa.2010.03.014