# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 5 Conclusions  
The proposed intelligent calligraphy evaluation model, based on deep learning with the Siamese structure as its fundamental framework, achieves the evaluation of hard-pen writing by calculating the similarity between the samples under evaluation and standard samples. The model utilizes an enhanced VGG16 as the backbone feature extraction network and introduces a Transformer structure to enhance the learning of interrelationships between feature maps, thereby improving the model's performance. Experimental results demonstrate that the Siamese structure is effective for the comparative evaluation of writing samples in hard-pen calligraphy. Additionally, the Transformer structure significantly enhances the evaluation model's performance.  
Considering the challenges posed by the weak representation of stroke forms and brushwork in hard-pen writing, the model assesses the quality of hard-pen Chinese character writing based on the overall feature similarity. This approach provides a novel method for the automatic evaluation of hard-pen calligraphy. Specifically designed for hard-pen calligraphy assessment, the model acquires its feature extraction capability through learning from the hard-pen calligraphy evaluation dataset, demonstrating strong specificity and adaptability. However, the model is subject to constraints, including the size of the training dataset, which is relatively small, and the limited diversity of the evaluation features.  
Future research could address these limitations in two ways. Firstly, by expanding the scale of the handwriting evaluation dataset, increasing the number of Chinese characters covered by the samples, and incorporating standard writing samples from renowned hard-pen calligraphers. Secondly, by integrating dynamic data from the process of hard-pen calligraphy writing as a dimension of features into the similarity evaluation, enriching the features for hard-pen calligraphy evaluation, and better reflecting individual differences in the process of hard-pen writing.  
Conceptualization, H.Z.; methodology, X.L., L.L. and F.Y.; resources, H.Z.; data curation, X.L.; writing\(-\)original draft preparation, X.L. and F.Y.; writing\(-\)review and editing, F.Y. and L.L.; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Mianyang Polytechnic Foundation for Science and Technology.  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors would like to acknowledge the editors and reviewers who provided valuable comments and suggestions that improved the quality of the manuscript.  
The authors declare that they do not have any conflicts of interest. This research does not involve any human or animal participation. All authors have checked and agreed with the submission.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **MAE** & **PCC** \\ \hline VGG16 & 0.090 & 0.610 \\ ResNet50 & 0.087 & 0.624 \\ VGG16 + transformer & 0.076 & 0.681 \\ ResNet50 + transformer & 0.085 & 0.637 \\ transformer & 0.079 & 0.662 \\ \hline \end{tabular}
\end{table}
Table 3: The results of Mean Absolute Error and Pearson Correlation Coefficient.