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This paper presents an attention-based row-column encoder-decoder (ARCED) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines.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, a row-column encoder, and a decoder. We introduce a row-column BLSTM in the encoder and a residual LSTM network in the decoder. The whole system is trained end-to-end by a standard cross-entropy loss function, requiring only document images and their ground-truth text. We experimentally evaluate the performance of ARCED on the dataset of Japanese historical documents: Kana-PRMU. The results of the experiments show that ARCED outperforms the state-of-the-art recognition methods on the dataset. Furthermore, we demonstrate that the row-column BLSTM in the encoder and the residual LSTM in the decoder improves the performance of the encoder-decoder model for the recognition of Japanese historical document.

Handwritten Japanese text recognition is still a big challenging task due to the large character set, diversity of writing styles, and multiple-touches between characters. In this paper, we propose a model of Attention Augmented Convolutional Recurrent Network (AACRN) for recognizing handwritten Japanese text lines. The AACRN model has three main parts: a convolutional feature extractor, a self-attention based encoder, and a CTC-decoder. The whole model can be trained end-to-end. In the experiment, we evaluate the performance of the AACRN model on the TUAT Kondate dataset and the Kuzushiji dataset. The results of the experiments show that the proposed model achieves higher performance than the state-of-the-art recognition accuracies on the test set of TUAT Kondate and the Kuzushiji dataset.

This paper presents an attention-based 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sequence to sequence (ACseq2seq) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines.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 Short-Term 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 end-to-end by a standard cross-entropy 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 state-of-the-art recognition methods on the anomalously deformed Kana datasets.

This paper presents an end-to-end model of Deep Convolutional Recurrent Network (DCRN) for recognizing offline handwritten Japanese text lines. The end-to-end DCRN model has three parts: a convolutional feature extractor using Deep Convolutional Neural Network (DCNN) to extract a feature sequence from a text line image; recurrent layers employing a Deep Bidirectional LSTM to predict pre-frame from the feature sequence; and a transcription layer using Connectionist Temporal Classification (CTC) to convert the pre-frame predictions into the label sequence. Since our end-to-end model requires a large data for training, we synthesize handwritten text line images from sentences in corpora and handwritten character patterns in the Nakayosi and Kuchibue database with elastic distortions. In the experiment, we evaluate the performance of the end-to-end model and the effectiveness of the synthetic data generation method on the test set of the TUAT Kondate database. The results of the experiments show that our end-to-end model achieves higher than the state-of-the-art recognition accuracy on the test set of TUAT Kondate with 96.35% and 98.05% character level recognition accuracies without and with the generated synthetic data, respectively.

This paper presents methods for three different tasks of recognizing anomalously deformed Kana in Japanese historical documents, which were contested by IEICE PRMU1 2017. The tasks have three levels: single character recognition, three Kana characters sequence recognition and unrestricted Kana recognition. We compare several methods for each task. For the level 1, we evaluate CNN based methods and BLSTM based methods. For the level 2, we consider several variations of a combined architecture of CNN and BLSTM. For the level 3, we compare an extension of the method for the level 2 and a segmentation based method. We achieve the single character recognition accuracy of 96.8%, the three Kana characters sequence recognition accuracy of 87.12% and the unrestricted Kana recognition accuracy of 73.3%. These results prove the performance of CNN and BLSTM on these tasks.

This paper presents a model of Deep Convolutional Recurrent Network (DCRN) for recognizing offline handwritten Japanese text lines without explicit segmentation of characters. Most of traditional offline handwritten Japanese text recognizers perform segmentation of text image into characters before individually recognizing each character. Although segmentation by recognition and context are employed to recover from segmentation errors, errors made at this stage directly make an impact on the performance of the whole system. The DCRN model consists of three parts: a convolutional feature extractor using Convolutional Neural Network (CNN) and sliding window to extract features from text image; recurrent layers using BLSTM to predict pre-frame from an input sequence; and a transcription layer using a CTC-decoder to translate the predictions into the label sequence. Experimental results on the database: TUAT Kondate database demonstrates the effectiveness of the proposed method.