A Study of Discriminative Training for HMM-Based Online Handwritten Chinese/Japanese Character Recognition
作者： Yongqiang Wang , Qiang Huo , Yu Shi
关键词:
摘要: We present a study of discriminative training classifiers using both maximum mutual information (MMI) and minimum classification error (MCE) criteria for online handwritten Chinese/Japanese character recognition based on continuous-density hidden Markov models. It is observed that MCE-trained can achieve much higher accuracy than MMI-trained ones. Benchmark results simplified Chinese, traditional Chinese Japanese characters are reported three tasks with vocabulary 9119, 20924, 12333 respectively.
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