Creating New Chinese Fonts based on Manifold Learning and Adversarial Networks

dc.contributor.authorGuo, Yuanen_US
dc.contributor.authorLian, Zhouhuien_US
dc.contributor.authorTang, Yingminen_US
dc.contributor.authorXiao, Jianguoen_US
dc.contributor.editorDiamanti, Olga and Vaxman, Amiren_US
dc.date.accessioned2018-04-14T18:32:49Z
dc.date.available2018-04-14T18:32:49Z
dc.date.issued2018
dc.description.abstractThe design of fonts, especially Chinese fonts, is known as a tough task that requires considerable time and professional skills. In this paper, we propose a method to easily generate Chinese font libraries in new styles based on manifold learning and adversarial networks. Starting from a number of existing fonts that cover various styles, we firstly use convolutional neural networks to obtain the representation features of these fonts, and then build a font manifold via non-linear mapping. Using the font manifold, we can interpolate and move between those existing fonts to get new font features, which are then fed into a generative network learned via adversarial training to generate the whole new font libraries. Experimental results demonstrate that high-quality Chinese fonts in various new styles against existing ones can be efficiently generated using our method.en_US
dc.description.sectionheadersMethods and Applications, User Studies
dc.description.seriesinformationEG 2018 - Short Papers
dc.identifier.doi10.2312/egs.20181045
dc.identifier.issn1017-4656
dc.identifier.pages61-64
dc.identifier.urihttps://doi.org/10.2312/egs.20181045
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20181045
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectLine and curve generation
dc.subjectI.2.4 [Artificial Intelligence]
dc.subjectLearning
dc.subjectConnectionism and neural nets
dc.titleCreating New Chinese Fonts based on Manifold Learning and Adversarial Networksen_US
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