Abstract
This paper proposed an end-to-end model for recognizing offline handwritten Mongolian words. To be specific, a sequence to sequence architecture with attention mechanism is used to perform the task of generating a target sequence from a source sequence. The proposed model consists of two LSTMs and one attention network. The first LSTM is an encoder which consumes a frame sequence of one word image. The second LSTM is a decoder which can generate a sequence of letters. The attention network is added between encoder and decoder, which allow the decoder to focus on different positions in a sequence of frames during the procedure of decoding. In this study, we have attempted two schemes for generating frames from word images. In the first scheme, frames are generated with overlapping. Each adjacent two frames overlap half a frame. In the second scheme, frames are generated without overlapping. In addition, the height of the frame is also taken into consideration in our study. By comparison, the better scheme for generating frames has been determined. Experimental results demonstrate that the proposed end-to-end model outperforms the state-of-the-art method.
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This paper is supported by the National Natural Science Foundation of China under Grant 61463038.
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Wei, H., Liu, C., Zhang, H., Bao, F., Gao, G. (2019). End-to-End Model for Offline Handwritten Mongolian Word Recognition. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_19
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