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.
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Acknowledgments
This work was supported by the National Science Foundation of China (Grant No. 61625204), partially supported by the State Key Program of National Science Foundation of China (Grant No. 61432012 and 61432014).
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Dai, F., Tang, C., Lv, J. (2018). Classification of Calligraphy Style Based on Convolutional Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_31
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DOI: https://doi.org/10.1007/978-3-030-04212-7_31
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