Triggered attention model for scene text recognition

Abstract

Scene text recognition has attracted the attention of many researchers owing to its widely application. Numerous methods have been proposed in this field and achieved unprecedented success. However, most of the datasets and algorithms are designed for English scene text recognition while only a few researches focus on the Chinese scene text recognition. Furthermore, despite the higher processing speed and ability of working as language model, attention-based mechanism suffers from the misalignment problem. Triggered attention model is proposed to tackle these problems. Taking character image as input, A triggered attention-based encoder-decoder network that outputs character sequence is proposed in this paper. Furthermore, an encoder network that takes colorful images as input is implemented to extract deep representations of input images. Experiments on various datasets show that the Triggered attention method substantially outperforms the existing attention based methods.