A Study on the Geometric Characteristics of Line Mobility and Structural Changes in the Aesthetic System of Chinese Calligraphy

As an important part of Chinese traditional culture and art, how to efficiently realize the recognition, retrieval and style appreciation of calligraphy is of great significance. Aiming at the shortcomings of the traditional geometric feature recognition model with low recognition efficiency, this paper applies morphological neural network to the geometric feature recognition of calligraphy to design a geometric feature recognition model for calligraphy. Image enhancement is performed on the calligraphic graphics, the expansion pooling subnet is designed to replace the maximum pooling layer, and the calligraphic geometric feature recognition network is constructed by combining the residual block structure. The average recognition accuracy of this model in the geometric feature refinement recognition task is as high as 97.23%, which is higher than that of the comparative models such as CNN, LeNet-5, and the recognition accuracies are not less than 96% for the Euclidean, Liu, Zhao, and Yan styles. Using the model of this paper to explore the influence of calligraphic line fluidity and structural changes on the geometric features, it is analyzed that the “line” has a more significant influence on the geometric features of calligraphy than the “structure”. In the six types of traditional calligraphy, such as large seal, small seal, official script, regular script, line script, and cursive script, cursive script is only similar to the geometric characteristics of line script, and the geometric characteristics are very unique.