The goal of text spotting is to perform text detection and recognition in an
end-to-end manner. Although the diversity of luminosity and orientation in
scene texts has been widely studied, the font diversity and shape variance of
the same character are ignored in recent works, since most characters in
natural images are rendered in standard fonts. To solve this problem, we
present a Chinese Artistic Dataset, termed as ARText, which contains 33,000
artistic images with rich shape deformation and font diversity. Based on this
database, we develop a deformation robust text spotting method (DR TextSpotter)
to solve the recognition problem of complex deformation of characters in
different fonts. Specifically, we propose a geometric prior module to highlight
the important features based on the unsupervised landmark detection
sub-network. A graph convolution network is further constructed to fuse the
character features and landmark features, and then performs semantic reasoning
to enhance the discrimination for different characters. The experiments are
conducted on ARText and IC19-ReCTS datasets. Our results demonstrate the
effectiveness of our proposed method.

通过构建具有形状变形和字体多样性的中国艺术数据集，提出了一种变形鲁棒的文本定位方法 (DR TextSpotter)，该方法利用几何先验模块和图卷积网络融合字符特征和标记特征，并进行语义推理以增强对不同字符的区分，实验证明了该方法的有效性。