Stroke Segmentation of Calligraphy Based on Conditional Generative Adversarial Network

As the essence of Chinese traditional art, brush calligraphy needs to continue to inherit and carry forward in the new era. Calligraphy is a complex figure composed of strokes as the basic unit. If you want to analyze the structure of calligraphy, stroke segmentation is the first step. The traditional stroke segmentation method mainly uses the refinement method to extract feature points from the Chinese character skeleton, and analyzes the sub-stroke topology relationship of the intersection region to segment the strokes. This paper analyzes the limitations of traditional stroke segmentation based on the underlying feature splitting strokes, and the strokes are directly segmented by using the adversarial learning mechanism of conditional generative adversarial network (CGAN). Improve the method of extracting strokes from first refinement and then segmentation to direct segmentation. This method can effectively extract accurate strokes. The resulting high-level semantic features and individual strokes that retain complete information are helpful for the subsequent evaluation of the outline and structure of calligraphy. © 2022 Science Press. All rights reserved.