Robust Character Stroke Segmentation For Diverse Fonts Via Contour Matching and Chain Propagation

Stroke segmentation is a fundamental technique for various character analysis and synthesis applications. However, existing methods often face challenges such as over-segmentation, under-segmentation, low segmentation accuracy, and limited generalization capability when segmenting characters of diverse fonts. To address these issues, we propose a novel stroke segmentation method based on contour matching and utilize a similarity-based chain propagation strategy to tackle the challenges posed by fonts with significant structural and stylistic variations. Extensive visual and quantitative experiments on a newly created high-quality dataset demonstrate that our approach outperforms state-of-the-art methods and effectively handles a wide range of fonts.