A Novel Three-Staged Generative Model for Skeletonizing Chinese Characters with Versatile Styles

Skeletons of characters provide vital information to support a variety of tasks,e.g.,optical character recogni-tion,image restoration,stroke segmentation and extraction,and style learning and transfer.However,automatically skele-tonizing Chinese characters poses a steep computational challenge due to the large volume of Chinese characters and their versatile styles,for which traditional image analysis approaches are error-prone and fragile.Current deep learning based approach requires a heavy amount of manual labeling efforts,which imposes serious limitations on the precision,robust-ness,scalability and generalizability of an algorithm to solve a specific problem.To tackle the above challenge,this paper introduces a novel three-staged deep generative model developed as an image-to-image translation approach,which signifi-cantly reduces the model's demand for labeled training samples.The new model is built upon an improved G-net,an en-hanced X-net,and a newly proposed F-net.As compellingly demonstrated by comprehensive experimental results,the new model is able to iteratively extract skeletons of Chinese characters in versatile styles with a high quality,which noticeably outperforms two state-of-the-art peer deep learning methods and a classical thinning algorithm in terms of F-measure,Hausdorff distance,and average Hausdorff distance.