基于支持向量机的手印平假名识别

Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition Pub Date : 2002-08-06 DOI:10.1109/IWFHR.2002.1030884

K. Maruyama, M. Maruyama, H. Miyao, Y. Nakano

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引用次数: 11

摘要

描述了一种基于支持向量机(SVM)的决策有向无环图(DDAG)提高模式识别累积识别率的方法。原始的DDAG虽然性能水平高，执行速度快，但没有考虑所谓的累积识别率。我们构造了一个包含累积识别率的DDAG。通过对JEITA-HP中手印平假名字符的实验，我们的累积识别率得到了提高，其执行时间与原始的DDAG几乎相同，比著名的支持向量机多类识别方法之一Max Win算法快30倍。

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Handprinted Hiragana recognition using support vector machines

Describes a method to improve the cumulative recognition rates of pattern recognition using a decision directed acyclic graph (DDAG) based on support vector machines (SVM). Though the original DDAG has high level of performance and its execution speed is fast, it does not consider the so-called cumulative recognition rate. We construct a DDAG which can incorporate the cumulative recognition rate. As a result of our experiment for handprinted Hiragana characters in JEITA-HP, the cumulative recognition rate is improved and its execution time is almost the same as the original DDAG and 30 times faster than the Max Win Algorithm which is one of the famous recognition methods using support vector machines for a multi-class problem.

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Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition

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