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Recently, hidden Markov models (HMMs) have achieved promising results foroffline handwritten Chinese text recognition. However, due to the largevocabulary of Chinese characters with each modeled by a uniform and fixednumber of hidden states, a high demand of memory and computation is required.In this study, to address this issue, we present parsimonious HMMs via thestate tying which can fully utilize the similarities among different Chinesecharacters. Two-step algorithm with the data-driven question-set is adopted togenerate the tied-state pool using the likelihood measure. The proposedparsimonious HMMs with both Gaussian mixture models (GMMs) and deep neuralnetworks (DNNs) as the emission distributions not only lead to a compact modelbut also improve the recognition accuracy via the data sharing for the tiedstates and the confusion decreasing among state classes. Tested on ICDAR-2013competition database, in the best configured case, the new parsimonious DNN-HMMcan yield a relative character error rate (CER) reduction of 6.2%, 25%reduction of model size and 60% reduction of decoding time over theconventional DNN-HMM. In the compact setting case of average 1-state HMM, ourparsimonious DNN-HMM significantly outperforms the conventional DNN-HMM with arelative CER reduction of 35.5%.

Parsimonious HMMs for Offline Handwritten Chinese Text Recognition