Unattached irregular scene text rectification with refined objective

Abstract

Recently, text recognition tasks have reached a high level with deep learning-based methods. The techniques are widely applied in different fields, and nowadays most researchers aim to build an effective approach to deal with irregular text in scene images. In this work, we propose a GAN-based framework to rectify scene text with rotation, curving, or other distortions. Unlike previous rectification modules that rely on the recognition networks, our model can be utilized either as an independent model or an extra component. Therefore, annotations of the text content are not required to train the model. And we utilize a refined training objective with the proposed sample loss, which is able to effectively control pixels in the output images that are supposed to be sampled from input ones. Experiments on public benchmarks demonstrate the effectiveness of our method. The code will be publicly available on github soon.