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Deep Deformable Artistic Font Style Transfer

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成果类型:
期刊论文
作者:
Zhu, Xuanying;Lin, Mugang;Wen, Kunhui;Zhao, Huihuang;Sun, Xianfang
通讯作者:
Mugang Lin
作者机构:
[Zhu, Xuanying; Zhao, Huihuang; Wen, Kunhui; Lin, Mugang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
[Zhao, Huihuang; Lin, Mugang] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
[Sun, Xianfang] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 4AG, Wales.
通讯机构:
[Mugang Lin] C
College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
style transfer;generative adversarial networks;deformable convolutional networks;artistic font generation
期刊:
Electronics
ISSN:
2079-9292
年:
2023
卷:
12
期:
7
页码:
1561-
基金类别:
This research was supported in part by the Scientific Research Fund of Hunan Provincial Education Department (22A0502), the National Natural Science Foundation of China (61772179), the Hunan Provincial Natural Science Foundation of China (2019JJ40005), the 14th Five-Year Plan Key Disciplines and Application-oriented Special Disciplines of Hunan Province (Xiangjiaotong [2022] 351), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Science and Technology Innovation Project of Hengyang(202250045231), the Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University (2022HSKFJJ012), and the Postgraduate Scientific Research Innovation Project of Hunan Province (QL20210262).
机构署名:
本校为第一机构
院系归属:
计算机科学与技术学院
摘要:
The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effect...

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