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Video character recognition improvement by support vector machines and regularized discriminant analysis  

Lim, Su-Yeol (Department of Statistics, Chonnam National Univisity)
Baek, Jang-Sun (Department of Statistics, Chonnam National Univisity)
Kim, Min-Soo (Department of Statistics, Chonnam National Univisity)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.4, 2010 , pp. 689-697 More about this Journal
Abstract
In this study, we propose a new procedure for improving the character recognition of text area extracted from video images. The recognition of strings extracted from video, which are mixed with Hangul, English, numbers and special characters, etc., is more difficult than general character recognition because of various fonts and size, graphic forms of letters tilted image, disconnection, miscellaneous videos, tangency, characters of low definition, etc. We improved the recognition rate by taking commonly used letters and leaving out the barely used ones instead of recognizing all of the letters, and then using SVM and RDA character recognition methods. Our numerical results indicate that combining SVM and RDA performs better than other methods.
Keywords
Character recognition; classification; regularized discriminant analysis; support vector machines;
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Times Cited By KSCI : 5  (Citation Analysis)
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