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 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.

中文翻译：

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通过学习字形的规范形式来增强场景字符识别

作为文档分析的基本问题之一，近几年来场景字符识别引起了人们的极大兴趣。但是由于存在许多无法控制的因素，包括字形转换，模糊，嘈杂的背景，照明不均匀等，该问题仍然被认为是极具挑战性的。基于以下事实：出现在场景图像中的字符都是从其对应的规范形式得出的。我们的主要观察结果是，与传统的基于分类的特征学习框架相比，可以通过解决专门设计的生成任务来学习更多区分特征。特别，我们设计了一个基于GAN的模型，以使给定场景角色的学习到的深度特征能够以多种标准字体样式重构相应的字形。以这种方式，我们获得了场景角色的深层特征，这些特征在识别上更具区分性，并且对上述因素不那么敏感。我们在几个公共数据库上进行的实验证明，与现有技术相比，我们的方法具有优越性。