A Two-Level Rectification Attention Network for Scene Text Recognition

Scene text recognition is a challenging task in the computer vision field due to the diversity of text styles and the complexity of the image backgrounds. In recent decades, numerous text rectification and recognition methods have been proposed to solve these problems. However, most of these methods rectify texts at the geometry level or pixel level. The former is limited by geometric constraints, and the latter is prone to blurring the text. In this paper, we propose a two-level rectification attention network (TRAN) to rectify and recognize texts. This network consists of two parts: a two-level rectification network (TORN) and an attention-based recognition network (ABRN). Specifically, the TORN first rectifies texts at the geometry level and then performs a pixel-level adjustment, which not only eliminates the geometric constraints but also renders clear texts. The ABRN’s role is to recognize text in the rectified images. To improve the feature extraction ability of our model, we design a new channel-wise and kernel-wise attention unit, which enables the network to handle significant variations of character size and channel interdependencies. Furthermore, we propose a skip training strategy to make our model converge smoothly. We conduct experiments on various benchmarks, including regular and irregular datasets. The experimental results show that our method achieves a state-of-the-art performance.