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.