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• W2900471805 abstract "Calligraphy is the cultural treasure of the Chinese nation for five millenniums, which has always been loved by the Chinese people. This paper collects a large number of characters of Chinese calligraphy and builds a Chinese characters calligraphy data set. By establishing three different Convolution Neural Network (CNN) models, the features of calligraphy handwriting are extracted. In addition, we use some techniques to improve the robustness and generalization ability of the CNN model, so that the model can adapt to more classification tasks. The experimental results prove that the proposed method can not only well identify the style of different calligraphers, but also have good performance in the classification of the font format of soft pen calligraphy and hard pen calligraphy." @default.
• W2900471805 created "2018-11-29" @default.
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• W2900471805 date "2018-01-01" @default.
• W2900471805 modified "2023-10-15" @default.
• W2900471805 title "Classification of Calligraphy Style Based on Convolutional Neural Network" @default.
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• W2900471805 doi "https://doi.org/10.1007/978-3-030-04212-7_31" @default.
• W2900471805 hasPublicationYear "2018" @default.
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