Scene text recognition (STR) has been extensively studied in last few years.
Many recently-proposed methods are specially designed to accommodate the
arbitrary shape, layout and orientation of scene texts, but ignoring that
various font (or writing) styles also pose severe challenges to STR. These
methods, where font features and content features of characters are tangled,
perform poorly in text recognition on scene images with texts in novel font
styles. To address this problem, we explore font-independent features of scene
texts via attentional generation of glyphs in a large number of font styles.
Specifically, we introduce trainable font embeddings to shape the font styles
of generated glyphs, with the image feature of scene text only representing its
essential patterns. The generation process is directed by the spatial attention
mechanism, which effectively copes with irregular texts and generates
higher-quality glyphs than existing image-to-image translation methods.
Experiments conducted on several STR benchmarks demonstrate the superiority of
our method compared to the state of the art.

本文介绍了一种基于注意力生成字形和可训练字体嵌入的方法，用于解决字体风格差异和排版不规则性在场景文本识别中带来的挑战，并通过实验证明了该方法的优越性。