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2017 | OriginalPaper | Chapter

Chinese Handwriting Generation by Neural Network Based Style Transformation

Authors : Bi-Ren Tan, Fei Yin, Yi-Chao Wu, Cheng-Lin Liu

Published in: Image and Graphics

Publisher: Springer International Publishing

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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.

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previous chapter Deep Networks for Single Image Super-Resolution with Multi-context Fusion
next chapter Text Detection Based on Affine Transformation
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Metadata
Title
Chinese Handwriting Generation by Neural Network Based Style Transformation
Authors
Bi-Ren Tan
Fei Yin
Yi-Chao Wu
Cheng-Lin Liu
Copyright Year
2017
Book
Image and Graphics

Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71607-7_36

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