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Intelligent Evaluation Method of Calligraphy Characters Based on Deep Stroke Extraction

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DOI: 10.23977/acss.2023.071014 | Downloads: 24 | Views: 347

Author(s)

Meng Li 1, Guanghao Ren 1

Affiliation(s)

1 Institute of Automation, Chinese Academy of Sciences, Beijing, China

Corresponding Author

Meng Li

ABSTRACT

Calligraphy is an important part of Chinese culture. And calligraphy education is the main way to spread calligraphy culture. Intelligent calligraphy evaluation can reduce dependence on experienced calligraphy teachers and effectively enhance the development of calligraphy culture. However, traditional intelligent calligraphy evaluation methods mostly focus on the whole and lack fine-grained analysis, which cannot form effective evaluation results. In this paper, we propose an intelligent evaluation method for calligraphy characters based on deep stroke extraction. By disassembling calligraphy character strokes, a more fine-grained evaluation of the writing results of a single stroke can be achieved. This method consists of two main parts: stroke extraction module that extracts single strokes through a structure deformable image registration-based stroke extraction model; evaluation module that provides detailed quantitative evaluation results from the whole character, radicals and single strokes. The experimental results show that our method can extract strokes of complex calligraphy characters and provide detailed evaluation results of calligraphy characters effectively.

KEYWORDS

Stroke Extraction, Evaluation of Calligraphy Characters, Deep Learning

CITE THIS PAPER

Meng Li, Guanghao Ren, Intelligent Evaluation Method of Calligraphy Characters Based on Deep Stroke Extraction. Advances in Computer, Signals and Systems (2023) Vol. 7: 99-106. DOI: http://dx.doi.org/10.23977/acss.2023.071014.

REFERENCES

[1] Wang, Z., Liao, M., & Maekawa, Z. (2016). A Study on Quantitative Evaluation of Calligraphy Characters. Computer Technology and Application, 2016(7), 103-122.
[2] Xu, Y., & Shen, R. (2023). Aesthetic Evaluation of Chinese Calligraphy: a Cross-cultural Comparative Study. Current Psychology, 42(27), 23096-23109.
[3] Xu, S., Jiang, H., Lau, F. C., & Pan, Y. (2012). Computationally Evaluating and Reproducing the Beauty of Chinese Calligraphy. IEEE Intelligent Systems, 27(03), 63-72.
[4] Wang, Z., Lyu, R., Liu, X., Ze, Y., Xie, Z., & Yan, T. (2022). Constructing Calligraphy Evaluation Model Based on Writing Movement with LSTM Network. In 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 158-163.
[5] Ma, Z., & Su, J. (2016). Aesthetics Evaluation for Robotic Chinese Calligraphy. IEEE Transactions on Cognitive and Developmental Systems, 9(1), 80-90.
[6] Xu, P., Wang, L., Guan, Z., Zheng, X., Chen, X., Tang, Z., ... & Wang, Z. (2018). Evaluating Brush Movements for Chinese Calligraphy: a Computer Vision Based Approach. In 27th International Joint Conference on Artificial Intelligence, IJCAI 2018, 1050-1056.
[7] Wang, M., Fu, Q., Wang, X., Wu, Z., & Zhou, M. (2016). Evaluation of Chinese Calligraphy By Using DBSC Vectorization and ICP Algorithm. Mathematical Problems in Engineering, 2016, 1–11.
[8] Jian, M., Dong, J., Gong, M., Yu, H., Nie, L., Yin, Y., & Lam, K. M. (2019). Learning the Traditional Art of Chinese Calligraphy Via Three-Dimensional Reconstruction and Assessment. IEEE Transactions on Multimedia, 22(4), 970-979.
[9] Sun, M., Gong, X., Nie, H., Iqbal, M. M., & Xie, B. (2023). SRAFE: Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy. CAAI Transactions on Intelligence Technology, 8(3), 1077-1086.
[10] Sun, Y., Qian, H., & Xu, Y. (2014). A Geometric Approach to Stroke Extraction for the Chinese Calligraphy Robot. In 2014 IEEE International Conference on Robotics and Automation (ICRA), 3207-3212.
[11] He, R., & Yan, H. (2000). Stroke Extraction As Pre-Processing Step to Improve Thinning Results of Chinese Characters. Pattern Recognition Letters, 21(9), 817-825.
[12] Li, M., Yu, Y., Yang, Y., Ren, G., & Wang, J. (2023). Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration. In Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 1360-1367.
[13] Wang, T. Q., Jiang, X., & Liu, C. L. (2022). Query Pixel Guided Stroke Extraction With Model-Based Matching for Offline Handwritten Chinese Characters. Pattern Recognition, 123, 108416.
[14] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural computation, 9(8), 1735-1780.
[15] Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Advances in Neural Information Processing Systems, 28.
[16] Kim, B., Wang, O., Öztireli, A. C., & Gross, M. (2018). Semantic Segmentation for Line Drawing Vectorization Using Neural Networks. In Computer Graphics Forum, 37(2), 329-338. 

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