Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes. Recently, the community has paid increasing attention to the problem of recognizing text instances of irregular shapes. One intuitive and effective solution to this problem is to rectify irregular text to a canonical form before recognition. However, these methods might struggle when dealing with highly curved or distorted text instances. To tackle this issue, we propose a Symmetry-constrained Rectification Network (ScRN) in this paper, based on the local attributes of text instances, such as center line, scale, and orientation. Such constraints with an accurate description of text shape enable ScRN to generate better rectification results than existing methods thus leading to higher recognition accuracy. Our method achieves state-of-the-art performance on text of both regular and irregular shapes. Specifically, the system outperforms existing algorithms by a large margin on datasets that contain quite a proportion of irregular text instances, e.g., ICDAR 2015, SVT-Perspective and CUTE80.
Furthermore, Yang et al. REF proposed a symmetry-constrained rectification network based on the rich local attributes to generate better rectification results.
Symmetry-constrained Rectification Network for Scene Text Recognition
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science