Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition

Scene text recognition has attracted rapidly increasing attention from the research community. Recent dominant approaches typically follow an attention-based encoder-decoder framework that uses a unidirectional decoder to perform decoding in a left-to-right manner, but ignoring equally important right-to-left grammar information. In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition. Firstly, the backward decoder performs decoding from right to left and generates the reverse language context. After that, the forward decoder simultaneously utilizes the visual context from image encoder and the reverse language context from backward decoder through two attention modules. In this way, the bidirectional decoders perform effective interaction to fully fuse the bidirectional grammar information and further improve the decoding quality. Besides, in order to relieve the adverse effect of noises, we devise a gated context mechanism to adaptively make use of the visual context and reverse language context. Extensive experiments on various challenging benchmarks demonstrate the effectiveness of our method.