Abstract
This paper improves the image classification task based on deep learning and proposes a new font grading system to help calligraphy lovers to practice calligraphy. The basic model of the framework proposed in this paper is ResNet, and then dilated convolution, deformable convolutional, and deformable pooling are used on the traditional ResNet to improve performance. Experimental results show that the proposed algorithm can make a reasonable judgment on handwriting.
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Acknowledgements
The paper is supported by the foundation of Fujian Province Educational Science “Thirteenth Five-Year Plan” 2018 Project—“Research on the college students’ anomie of online courses learning and intervention of their online courses learning” (No. FJJKCGZ18-850, Key funding project), Young and Middle-aged Teacher Educational and Scientific Research Project of Fujian Province—“Research on the college students’ anomie of online courses learning and intervention of their online courses learning”, and the Teaching Reform Research Project of Minjiang University in 2018—“The Interventional Teaching Reform aimed at the online courses learning anomie of college students” (No. MJU2018A005).
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Liu, S., Wang, Z., Wang, C., Zheng, J., Zhang, F. (2020). Preliminary Design and Application Prospect of Single Chinese Character Calligraphy Image Scoring Algorithm. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_46
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DOI: https://doi.org/10.1007/978-981-13-9710-3_46
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