WoodblockPrinting Mongolian Words Recognition by BiLSTM with Attention Mechanism Woodblockprinting Mongolian documents are seriously degraded due to aging. Therefore, it is difficult to segment woodblockprinting Mongolian words are into individual glyphs. In this paper, a holistic recognition approach based on sequence to sequence model has been proposed for the woodblockprinting Mongolian words. The input of the proposed model is the sequence of frames of a woodblock 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 bidirectional Long ShortTerm Memory (BiLSTM). The decoder consists of a Long ShortTerm 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.

An AttentionBased EndtoEnd Model for Multiple Text Lines Recognition in Japanese Historical Documents This paper presents an attentionbased convolutional sequence to sequence (ACseq2seq) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines. The recognition system has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract a feature sequence from an input image; an encoder employing bidirectional Long ShortTerm Memory (BLSTM) to encode the feature sequence; and a decoder using a unidirectional LSTM with the attention mechanism to generate the final target text based on the attended pertinent features. We also introduce a residual LSTM network between the attention vector and softmax layer in the decoder. The system can be trained endtoend by a standard crossentropy loss function. In the experiment, we evaluate the performance of the ACseq2seq model on the anomalously deformed Kana datasets in the PRMU contest. The results of the experiments show that our proposed model achieves higher recognition accuracy than the stateoftheart recognition methods on the anomalously deformed Kana datasets.