Skeleton-guided vectorization of Chinese calligraphy images

How to automatically generate compact and high-quality vectorization for Chinese calligraphy images is a challenging problem, since these images usually suffer from noisy contours and discontinuous strokes. In this paper, we propose a skeleton guided approach to vectorize Chinese calligraphy images. Since the skeleton reflects the writing trace and it is less influenced by contour noises, our method could extract the important writing style from the noisy contours. Specifically, in our method, the calligraphy image is first preprocessed by binarization and denoising. Then salient contour points are detected by a novel algorithm. Afterwards, under the guidance of skeleton information, the salient points are classified into corner points and joint points. Finally, a dynamic curve fitting procedure is applied to generate the vectorization result. Experimental results demonstrate that our skeleton-guided approach could automatically distinguish tiny features from contour noises and thus obtains more visually satisfactory performance compared to other existing methods.