{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chinese-typography-transfer","title":"Chinese Typography Transfer","arxiv_id":"1707.04904","date":"2017-07-16","proceeding":null,"authors":["Jie Chang","Yujun Gu"],"abstract":"In this paper, we propose a new network architecture for Chinese typography\ntransformation based on deep learning. The architecture consists of two\nsub-networks: (1)a fully convolutional network(FCN) aiming at transferring\nspecified typography style to another in condition of preserving structure\ninformation; (2)an adversarial network aiming at generating more realistic\nstrokes in some details. Unlike models proposed before 2012 relying on the\ncomplex segmentation of Chinese components or strokes, our model treats every\nChinese character as an inseparable image, so pre-processing or\npost-preprocessing are abandoned. Besides, our model adopts end-to-end training\nwithout pre-trained used in other deep models. The experiments demonstrates\nthat our model can synthesize realistic-looking target typography from any\nsource typography both on printed style and handwriting style.","url_abs":"http://arxiv.org/abs/1707.04904v2","url_pdf":"http://arxiv.org/pdf/1707.04904v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chinese-typography-transfer","repo_url":"https://github.com/cyy1998/Chinese-Transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.04904","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}