Recognition of Japanese historical text lines by an attention-based encoder-decoder and text line generation

Inspired by the recent successes of attention based encoder-decoder (AED) approach on image captioning, machine translation, we present an AED model as an end-to-end recognition system for recognizing Japanese historical documents. The recognition system has two main modules: a dense convolution neural network for extracting features, and a Long Shor Term Memory (LSTM) decoder integrating with attention model for generating target text. We can train the model end-to-end. The model requires only input text line images and corresponding output characters. Therefore, we don't need annotations for characters and save a lot of time for making annotations. We also present a method to generate artificial text lines to solve the imbalance problem of the current annotated database. The results of experiments on the annotated and artificial databases demonstrate the effectiveness of the text line generation. Our recognition system achieved Character Error Rate of 23.76% and 22.52% by training with and without artificial text lines, respectively. Moreover, our recognition system outperforms the CNN-LSTM system, which achieved the state-of-art results in other document recognition tasks.