UM
Detecting Chinese calligraphy style consistency by deep learning and one-class SVM
Jiulong Z.3; Luming G.3; Su Y.3; Sun X.1; Li X.4
2017-07-18
Source Publication2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017
Pages83-86
AbstractWhen beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural network model, we discriminate correctly styled and alien styled characters using a trained one-class support vector machine. Thus we can pick out those outliers. The proposed algorithm reaches satisfactory results. The algorithm can also be applied to other image style detection problems.
KeywordCalligraphy style Chinese calligraphy Deep learning Feature extraction
DOI10.1109/ICIVC.2017.7984523
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.University of Chinese Academy of Sciences
2.Fudan University
3.Xi'an University of Technology
4.Universidade de Macau
Recommended Citation
GB/T 7714
Jiulong Z.,Luming G.,Su Y.,et al. Detecting Chinese calligraphy style consistency by deep learning and one-class SVM[C],2017:83-86.
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