# Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs

## TL;DR

This paper introduces a GAN-based approach that learns canonical glyph forms to improve scene character recognition, making features more discriminative and robust against various image distortions.

## Contribution

It proposes a novel GAN-based method to learn canonical glyph representations, enhancing recognition accuracy over traditional classification methods.

## Key findings

- Outperforms state-of-the-art methods on multiple datasets.
- Deep features become more discriminative and robust.
- Effective against transformations, blur, noise, and illumination variations.

## Abstract

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.