  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 with
irregular shapes. One intuitive and effective way to handle 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 in this paper a
Symmetry-constrained Rectification Network (ScRN) based on 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 and thus lead to higher recognition
accuracy. Our method achieves state-of-the-art performance on text with 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.
