Deformation Robust Text Spotting with Geometric Prior
Xixuan Hao Aozhong Zhang Xianze Meng Bin Fu
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.