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Noise Removal using Support Vector Regression in Noisy Document Images
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 Title & Authors
Noise Removal using Support Vector Regression in Noisy Document Images
Kim, Hee-Hoon; Kang, Seung-Hyo; Park, Jai-Hyun; Ha, Hyun-Ho; Lim, Dong-Hoon;
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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.
 Keywords
Cross-validation;grid search;support vector regression;noise removal;
 Language
Korean
 Cited by
1.
최적의 서포트 벡터 머신을 이용한 유방암 분류,임진수;손진영;손주태;임동훈;

Journal of health informatics and statistics, 2013. vol.38. 1, pp.108-121
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