Stroke Extraction of Chinese Character Based on Deep Structure
  Deformable Image Registration
Meng Li Yahan Yu Yi Yang Guanghao Ren Jian Wang
Stroke extraction of Chinese characters plays an important role in the field
of character recognition and generation. The most existing character stroke
extraction methods focus on image morphological features. These methods usually
lead to errors of cross strokes extraction and stroke matching due to rarely
using stroke semantics and prior information. In this paper, we propose a deep
learning-based character stroke extraction method that takes semantic features
and prior information of strokes into consideration. This method consists of
three parts: image registration-based stroke registration that establishes the
rough registration of the reference strokes and the target as prior
information; image semantic segmentation-based stroke segmentation that
preliminarily separates target strokes into seven categories; and
high-precision extraction of single strokes. In the stroke registration, we
propose a structure deformable image registration network to achieve
structure-deformable transformation while maintaining the stable morphology of
single strokes for character images with complex structures. In order to verify
the effectiveness of the method, we construct two datasets respectively for
calligraphy characters and regular handwriting characters. The experimental
results show that our method strongly outperforms the baselines. Code is
available at https://github.com/MengLi-l1/StrokeExtraction.