SLOAN: Scale-Adaptive Orientation Attention Network for Scene Text Recognition

Abstract

Scene text recognition, the final step of the scene text reading system, has made impressive progress based on deep neural networks. However, existing recognition methods devote to dealing with the geometrically regular or irregular scene text. They are limited to the semantically arbitrary-orientation scene text. Meanwhile, previous scene text recognizers usually learn the single-scale feature representations for various-scale characters, which cannot model effective contexts for different characters. In this paper, we propose a novel scale-adaptive orientation attention network for arbitrary-orientation scene text recognition, which consists of a dynamic log-polar transformer and a sequence recognition network. Specifically, the dynamic log-polar transformer learns the log-polar origin to adaptively convert the arbitrary rotations and scales of scene texts into the shifts in the log-polar space, which is helpful to generate the rotation-aware and scale-aware visual representation. Next, the sequence recognition network is an encoder-decoder model, which incorporates a novel character-level receptive field attention module to encode more valid contexts for various-scale characters. The whole architecture can be trained in an end-to-end manner, only requiring the word image and its corresponding ground-truth text. Extensive experiments on several public datasets have demonstrated the effectiveness and superiority of our proposed method.