Chinese Handwriting Generation by Neural Network Based Style Transformation
Bi-Ren Tan1; Fei Yin1; Yi-Chao Wu1; Cheng-Lin Liu2
2017-12
会议名称International Conference on Image and Graphics
会议录名称Springer
会议日期2017年9月
会议地点中国上海
摘要 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.
 
关键词Handwriting Generation Style Transformation Neural Network Learning
学科领域模式识别
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/15621
专题模式识别国家重点实验室_模式分析与学习
通讯作者Cheng-Lin Liu
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
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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