Aesthetic evaluation of handwritten Chinese characters based on deep learning

The aesthetic evaluation of handwritten Chinese characters has important due value in primary and secondary education. This paper proposes to use deep learning to solve this problem. Firstly, using the public handwritten Chinese character aesthetic scoring dataset, training is carried out in various deep learning network models, and finally the network model with better scoring effect is determined. The experimental results show that the deep learning network model used in this paper has an accuracy rate as high as 83% in the aesthetic evaluation of Chinese characters, and the error performance is better than that of human evaluation.