近日，实验室一篇论文被国际顶级会议ACM MM 2020接收，信息如下：
论文名称：Exploring Font-independent Features for Scene Text Recognition
作者列表： Y. Wang, Z. Lian*
摘要：
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
addressthisproblem,weexplorefont-independentfeaturesofscene
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.