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Proceedings Paper

Research on classifying performance of SVMs with basic kernel in HCCR
Author(s): Limin Sun; Zhaoxin Gai
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Paper Abstract

It still is a difficult task for handwritten chinese character recognition (HCCR) to put into practical use. An efficient classifier occupies very important position for increasing offline HCCR rate. SVMs offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. As we know, the performance of SVM largely depends on the kernel function. In this paper, we investigated the classification performance of SVMs with various common kernels in HCCR. We found that except for sigmoid kernel, SVMs with polynomial kernel, linear kernel, RBF kernel and multi-quadratic kernel are all efficient classifier for HCCR, their behavior has a little difference, taking one with another, SVM with multi-quadratic kernel is the best.

Paper Details

Date Published: 17 February 2006
PDF: 8 pages
Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641O (17 February 2006); doi: 10.1117/12.642553
Show Author Affiliations
Limin Sun, Yantai Univ. (China)
Zhaoxin Gai, Yantai Univ. (China)


Published in SPIE Proceedings Vol. 6064:
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Nasser M. Nasrabadi; Edward R. Dougherty; Jaakko T. Astola; Syed A. Rizvi; Karen O. Egiazarian, Editor(s)

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