实验室一篇论文被文档分析与处理领域国际重要SCI期刊 International Journal on Document Analysis and Recognition (IJDAR)录用发表，信息如下：
论文名称：Boosting Scene Character Recognition by Learning Canonical Forms of Glyphs
作者列表：Yizhi Wang, Zhouhui Lian*, Yingmin Tang, Jianguo Xiao
摘要：
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 classi cation-based feature learning frameworks. Speci cally, 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.