Chinese Handwriting Generation By Neural Network Based Style Transformation
IMAGE AND GRAPHICS (ICIG 2017), PT I(2017)
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.
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关键词
Handwriting generation,Style transformation,Neural network learning
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