Dynamic Receptive Field Adaptation for Attention-Based Text Recognition

Existing attention-based recognition methods generally assume that the character scale and spacing in the same text instance are basically consistent. However, this hypothesis not always hold in the context of complex scene images. In this study, we propose an innovative dynamic receptive field adaption (DRA) mechanism for recognizing scene text robustly. Our DRA introduces different levels of receptive field features for classifying character and designs a novel way to explore historical attention information when calculating attention map. In this way, our method can adaptively adjust receptive field according to the variations of character scale and spacing in a scene text. Hence, our DRA mechanism can generate more informative features for recognition than traditional attention-based mechanisms. Notablely, our DRA mechanism can be easily generalized to off-the-shelf attention-based methods in text recognition to improve their performances. Extensive experiments on various public available benchmarks, including the IIIT-5K, SVT, SVTP, CUTE80, and ICDAR datasets, indicate the effectiveness and robustness of our method against the state-of-the art methods.