Algorithm for simulating calligrapher’s stroke features using neural networks
Abstract
In the field of calligraphy art, simulating the stroke features of calligraphers has always been a challenging task. Traditional methods often rely on manually designed rules or feature extraction algorithms, which are difficult to accurately capture calligraphy details and time-consuming. This article aimed to explore a more effective method to simulate the stroke features of calligraphers using neural network technology. This article mainly explored the simulation algorithm of calligrapher stroke features based on neural networks, including calligrapher stroke feature statistics, calligrapher stroke feature abstraction, and calligrapher stroke feature extraction. By using Convolutional Neural Network (CNN) to learn and analyze calligraphy works, a better performance neural network model was established to achieve automatic recognition and classification of calligrapher stroke features. By continuously optimizing model parameters, the accuracy of calligrapher stroke feature simulation can be improved. The average similarity rates for imitating stroke features of five calligraphers (regular script, cursive script, clerical script, seal script, and running script) using Artificial Neural Network (ANN) were 0.72, 0.62, 0.40, 0.33, and 0.53, respectively. The average similarity rates for imitating stroke features of 5 calligraphers (regular script, cursive script, clerical script, seal script, and running script) using CNN were 0.93, 0.78, 0.87, 0.67, and 0.80, respectively. The research results of this article promoted the inheritance and innovation of calligraphy art, and expanded the expression forms and application fields of calligraphy art.
References
1. Wang Luya, and Nor Azlin Hamidon. “Aesthetic and Value Study of Inscriptions from the Perspective of Calligraphy.” Art and Performance Letters 4.11 (2023): 44-49.
2. Yang Lijie, Zhan Wu, Tianchen Xu, Jixiang Du ,Enhua Wu . “Easy recognition of artistic Chinese calligraphic characters.” The Visual Computer 39.8 (2023): 3755-3766.
3. Yifan Zhang. “On the End and Core of Chinese Traditional Calligraphy Art.” International Journal of Advanced Culture Technology 11.2 (2023): 178-185.
4. Tang Xingjia, Penchang Zhang,Jian Du,Zongben Xu. “Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network.” Spectroscopy Letters 54.9 (2021): 645-664.
5. Guo Dongmei, Liang Ye; Guang Yan. “CCD-BSM: composite-curve-dilation brush stroke model for robotic chinese calligraphy.” Applied Intelligence 53.11 (2023): 14269-14283.
6. Luo Guoliang, Tingsong Lu, Haibin Xia, Shicong Hu, Shihui Guo. “Learning Chinese Calligraphy in VR With Sponge-Enabled Haptic Feedback.” Interacting with Computers 35.4 (2023): 530-542.
7. Si, Huihui. “Analysis of calligraphy Chinese character recognition technology based on deep learning and computer-aided technology.” Soft Computing 28.1 (2024): 721-736.
8. Li Rui, Xiaofei Jia, Changle Zhou, Junsong Zhang. “Reconfiguration of the brain during aesthetic experience on Chinese calligraphy—Using brain complex networks.” Visual Informatics 6.1 (2022): 35-46.
9. Lyu Ruimin, Tianqin Zhang, and Zhaolin Yuan. “Imaginary stroke movement measurement and visualization.” Proceedings of the ACM on Computer Graphics and Interactive Techniques 4.2 (2021): 1-12.
10. Cui, Wenyi, and Kohei Inoue. “Chinese calligraphy recognition system based on convolutional neural network.” ICIC Express Letters 15.11 (2021): 1187-1195.
11. Zhang, Xingxing. “Some Problems about Calligraphy.” Learning & Education 10.2 (2021): 88-89.
12. Song G. English translation of Chinese calligraphic aesthetics[J]. Babel, 2023, 69(1): 1-19.
13. Hung, Ruyu. “Self-cultivation through the art of calligraphy: from past to the future.” Beijing International Review of Education 4.3 (2022): 396-407.
Copyright (c) 2024 Xiaojun Zhu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
