Woodblock-Printing Mongolian Words Recognition by Bi-LSTM with Attention Mechanism

Woodblock-printing Mongolian documents are seriously degraded due to aging. Therefore, it is difficult to segment woodblock-printing Mongolian words are into individual glyphs. In this paper, a holistic recognition approach based on sequence to sequence model has been proposed for the woodblock-printing Mongolian words. The input of the proposed model is the sequence of frames of a wood-block printing Mongolian word. In order to generating the corresponding sequence of frames, each word image should be normalized into the same sizes in advance. And then, each word image is segmented into several fragments with equal size along writing direction. The output of the proposed model is a sequence of letters. To be specific, the proposed model contains three parts: an encoder, a decoder and an attention network. The encoder consists of a deep neural network and a bi-directional Long Short-Term Memory (Bi-LSTM). The decoder consists of a Long Short-Term Memory (LSTM) with a softmax layer. The encoder and decoder are connected by an attention network, which can map multiple frames to one letter. Experimental results demonstrate that the proposed approach outperforms the segmentation based method.