## 5 Conclusions  
The authentication of calligraphy works is a task that heavily relies on the subjective view of calligraphers and does not have clear mathematical indicators to quantify the similarity of calligraphy works. Furthermore, calligraphers intentionally add variations in character forms during creation to pursue artistic value in the overall work. Therefore, using neural networks to identify the authenticity of calligraphy works is difficult and poses a significant challenge to model algorithms.  
This paper proposes an algorithm for identifying the authenticity of calligraphy works. It uses projection and centroid algorithms to extract individual calligraphy characters from the works and expands the calligraphy dataset using data augmentation. Based on the EfficientNetv2-S model, the CBAM and Self-Attention modules improve network attention. The improved CBAM-EfficientNet and SA-EfficientNet networks are trained, and the effects are compared with EfficientNetv2-S, InceptionResNetv2, Inceptionv3, MobileNetv3-L, ResNet50, and MobileNet networks. The effects of the Kai script and Cao script models are tested using authentic calligraphy, other calligraphy works, and imitations of other calligraphers as the test set.  
The experimental results show that the model can learn the personal style characteristics of calligraphers from the calligraphy dataset and identify authentic works, imitations, and works by other calligraphers in the test set. Among them, the improved CBAM-EfficientNet and SA-EfficientNet based on the Efficientv2-S model achieve better authentication results compared to the original model in this project. Additionally, SA-EfficientNet achieves better results than CBAM-EfficientNet with a smaller model size and, overall, outperforms other networks. Therefore, the improvement based on the attention module has a good effect on identifying calligraphy authenticity.  
The methods described in this paper mainly aim at works with more obvious personal styles like calligraphy. However, these methods can be extended and applied to more fields. Using neural networks to identify individual handwriting has good application prospects in judiciary, criminal investigation, banking, and cultural relic authentication.  
Conceptualization, X.J., J.C. and Z.H.; methodology, W.W. and H.Y.; software, X.W. and Z.H.; validation, X.W., H.Y. and Z.H.; formal analysis, X.W., H.Y. and J.C.; investigation, J.C., X.J. and Z.H.; resources, [PERSON], J.C. and Z.H.; data curation, J.C., Z.H. and W.W.; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON], [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key Research and Development Project of China (grant number: 2018 YFA0902900), the Basic Research Program of Guangzhou City of China (grant number 202201011692), and the Guangdong Water Conservancy Science and Technology Innovation Project (grant number 2023-03).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to express their thanks to the Guangzhou Institute of Advanced Technology for helping them with the experimental characterization.  
The authors declare no conflicts of interest.