Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs
This addresses scene character recognition for document analysis, offering an incremental improvement by enhancing feature learning through generative tasks.
The paper tackles the challenging problem of scene character recognition by proposing a method to learn canonical forms of glyphs, resulting in more discriminative and robust features that demonstrate superiority over state-of-the-art methods in experiments on public databases.
As one of the fundamental problems in document analysis, scene character recognition has attracted considerable interests in recent years. But the problem is still considered to be extremely challenging due to many uncontrollable factors including glyph transformation, blur, noisy background, uneven illumination, etc. In this paper, we propose a novel methodology for boosting scene character recognition by learning canonical forms of glyphs, based on the fact that characters appearing in scene images are all derived from their corresponding canonical forms. Our key observation is that more discriminative features can be learned by solving specially-designed generative tasks compared to traditional classification-based feature learning frameworks. Specifically, we design a GAN-based model to make the learned deep feature of a given scene character be capable of reconstructing corresponding glyphs in a number of standard font styles. In this manner, we obtain deep features for scene characters that are more discriminative in recognition and less sensitive against the above-mentioned factors. Our experiments conducted on several publicly-available databases demonstrate the superiority of our method compared to the state of the art.