An AttentionBased EndtoEnd Model for Multiple Text Lines Recognition in Japanese Historical Documents This paper presents an attentionbased convolutional sequence to sequence (ACseq2seq) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines.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.

Evaluating SequencetoSequence Models for Handwritten Text Recognition Encoderdecoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attentionbased sequencetosequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained endtoend and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the stateoftheart without the use of a language model, and we significantly improve over any recent sequencetosequence approaches.