DNN-HMM for Large Vocabulary Mongolian Offline Handwriting Recognition

In this paper, we propose a large vocabulary Mongolian offline handwriting recognition system, using hidden Markov models (HMMs)-deep neural networks (DNN) hybrid architectures which shows superior performance on auto speech recognize (ASR) tasks. We select 50 sub-characters from all shape of Mongolian letters as the smallest modeling unit. First, a set of intensity features are extracted from each of the segmented word, which is based on a sliding window moving across each word image. Then, Multiple context dependent Gaussian mixture model (GMM)-HMMs are trained by the features. At last a DNN which have 4 hidden layers are trained as a frame classifier, where the class labels are state labels assigned to each input frame through forced alignment using the context-dependent model. In order to validate the proposed model, extensive experiments were carried out using the MHW database which contains 100,000 handwritten words in training set, 5,000 in test set I and 14,085 in Test set II. The DNN-HMM which is trained on raw image pixels yields best performance on Test set I with an accuracy of 97.61% and on Test set II with an accuracy of 94.14%.