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Chinese Handwriting Generation by Neural Network Based Style Transformation
Bi-Ren Tan1; Fei Yin1; Yi-Chao Wu1; Cheng-Lin Liu2
2017-12
Conference NameInternational Conference on Image and Graphics
Source PublicationSpringer
Conference Date2017年9月
Conference Place中国上海
Abstract This paper proposes a novel learning-based approach to generate personal style handwritten characters. Given some training characters written by an individual, we first calculate the deformation of corresponding points between the handwritten characters and standard templates, and then learn the transformation of stroke trajectory using a neural network. The transformation can be used to generate handwritten characters of personal style from standard templates of all categories. In training, we use shape context features as predictors, and regularize the distortion of adjacent points for shape smoothness. Experimental results on online Chinese handwritten characters show that the proposed method can generate personal-style samples which appear to be naturally written.
 
KeywordHandwriting Generation Style Transformation Neural Network Learning
Subject Area模式识别
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15621
Collection模式识别国家重点实验室_模式分析与学习
Corresponding AuthorCheng-Lin Liu
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
Bi-Ren Tan,Fei Yin,Yi-Chao Wu,et al. Chinese Handwriting Generation by Neural Network Based Style Transformation[C],2017.
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Chinese Handwriting (409KB)会议论文 开放获取CC BY-NC-SAView Download
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