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17 February 2006 Research on classifying performance of SVMs with basic kernel in HCCR
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Limin Sun and Zhaoxin Gai "Research on classifying performance of SVMs with basic kernel in HCCR", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641O (17 February 2006);

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