An attention-based row-column encoder-decoder model for text recognition in Japanese historical documents

摘要

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row-column encoder-decoder (ARCED) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines.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 We experimentally evaluate the performance of ARCED on the dataset of Japanese historical documents: Kana-PRMU.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the residual LSTM in the decoder improves the performance of the encoder-decoder model for the recognition of Japanese historical document.