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A Font Style Learning and Transferring Method Based on Strokes and Structure of Chinese Characters

机译:基于笔画和汉字结构的字体学习和转移方法

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The existing method of contour-based font description is difficult to meet the personalized need for various style font generations because of the large size of Chinese character set. In this paper, we propose a novel glyph description method which treats the Chinese character as a constitution of the stable part called 'structure' and the mutable part called 'style'. The structures of all characters are clustered by an improved K-Medoids method to guide the following generation of sample set which covers all kinds of style information of the whole character set. The result of cluster procedure indicates that radicals are bottlenecks for the reduction of sample set due to the low repetition rate in all characters. To address this problem, we present the radicals as a set of stroke-to-stroke layout structures, and render them from these substructures available in the sample set. Experiment results shows that the substitution enables us to learn the style information from a small set of sample characters (less than 10% of total amount) and generate the rest with the similar writing style.
机译:现有的基于轮廓的字体描述方法由于汉字集的大尺寸而难以满足各种样式字体代的个性化需求。在本文中,我们提出了一种新的字形描述方法,该方法将汉字作为稳定部分(称为“结构”)和可变部分(称为“样式”)的构成。通过改进的K-Medoids方法将所有字符的结构聚类,以指导下一代样本集的生成,该样本集涵盖整个字符集的各种样式信息。聚类过程的结果表明,由于所有字符的重复率都较低,所以部首是减少样本集的瓶颈。为了解决这个问题,我们将部首表示为一组笔画到笔画的布局结构,并从样本集中可用的这些子结构中将其渲染。实验结果表明,该替换使我们能够从一小组样本字符(少于总数的10%)中学习样式信息,并以类似的书写样式生成其余样式。

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