Artistic text recognition is an extremely challenging task with a wide range
of applications. However, current scene text recognition methods mainly focus
on irregular text while have not explored artistic text specifically. The
challenges of artistic text recognition include the various appearance with
special-designed fonts and effects, the complex connections and overlaps
between characters, and the severe interference from background patterns. To
alleviate these problems, we propose to recognize the artistic text at three
levels. Firstly, corner points are applied to guide the extraction of local
features inside characters, considering the robustness of corner structures to
appearance and shape. In this way, the discreteness of the corner points cuts
off the connection between characters, and the sparsity of them improves the
robustness for background interference. Secondly, we design a character
contrastive loss to model the character-level feature, improving the feature
representation for character classification. Thirdly, we utilize Transformer to
learn the global feature on image-level and model the global relationship of
the corner points, with the assistance of a corner-query cross-attention
mechanism. Besides, we provide an artistic text dataset to benchmark the
performance. Experimental results verify the significant superiority of our
proposed method on artistic text recognition and also achieve state-of-the-art
performance on several blurred and perspective datasets.

提出了一种利用角点、字符对比损失、Transformer 和全局特征建模方法的艺术文本识别方法，并建立了一个新的数据集来衡量性能，实验结果显示该方法在艺术文本识别上显著优于现有方法且在几个数据集上取得了最优性能。