Wang, W.; Jiang, X.; Yuan, H.; Chen, J.; Wang, X.; Huang, Z. Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network. Appl. Sci.2024, 14, 295.
Wang, W.; Jiang, X.; Yuan, H.; Chen, J.; Wang, X.; Huang, Z. Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network. Appl. Sci. 2024, 14, 295.
Wang, W.; Jiang, X.; Yuan, H.; Chen, J.; Wang, X.; Huang, Z. Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network. Appl. Sci.2024, 14, 295.
Wang, W.; Jiang, X.; Yuan, H.; Chen, J.; Wang, X.; Huang, Z. Research on Algorithm for Authenticating the Authenticity of Calligraphy Works Based on Improved EfficientNet Network. Appl. Sci. 2024, 14, 295.
Abstract
Calligraphy works have high artistic value, but there is a rampant problem of forgery. Indeed, authentication of traditional calligraphy heavily relies on calligraphers' subjective judgment. Therefore, spurred by the recent development of neural networks, this paper proposes a method for authenticating calligraphy works based on an improved EfficientNet network. Specifically, the developed method utilizes the character box algorithm and the centroid algorithm to extract individual calligraphy characters, which are then augmented and used as the training set for the model. The training process employs CBAM and Self-Attention modules to enhance the attention mechanism of the EfficientNet network. The model is tested on authentic works, imitated works, and works from other calligraphers and is compared with other networks. The experimental results demonstrate that the proposed method effectively achieves the authentication of calligraphy works, and the improved CBAM-EfficientNet network and SA-EfficientNet network achieve better authentication performance.
Keywords
Calligraphy work authentication; neural networks; attention mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
