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