An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

摘要

This paper presents a cascaded Hidden Markov Model (HMM), which allows state's transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character (HCC) and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used for training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples. Compared with the Segment HMMs approach, the recognition rate of this model for the first candidate is 87.89% and the error rate could be reduced by 12.4%.