# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
## 3 Results
### Experimental Results and Ablation Experiments  
The experimental results show the model training loss curve as shown in Figure 5. From the curve trend, it can be observed that the loss value decreases rapidly at the beginning of training, indicating that the model is optimized  
Additionally, to further validate the reliability of the results, we incorporated the \(F1\) score, Recall, and the Kappa coefficient for classification performance evaluation. The \(F1\) score is the harmonic mean of precision and recall, calculated as:  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}} \tag{8}\]  
Here, precision represents the proportion of true positives among the samples predicted as positive, while recall measures the proportion of true positives correctly identified by the model, defined as:  
\[\text{Recall}=\frac{\text{True}\,\text{Positives}}{\text{True}\,\text{Positives}+ \text{False}\,\text{Negatives}} \tag{9}\]  
Given the large number of categories and imbalanced sample distribution in our dataset, we introduced the Kappa coefficient to assess classifier agreement. The Kappa coefficient compares observed agreement with random agreement, eliminating the influence of chance, and is calculated as:  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{10}\]  
From Table 3, the MSSE model attains 98.6% accuracy and 94.3% Kappa, reflecting strong consistency. Its 96.9% recall and 95.7% \(F1\) score confirm robust minority class detection and precision-recall balance, proving its efficacy in assessing writing accuracy. To enhance the reliability of our experimental results, we conducted a set of Monte Carlo experiments on our self-constructed dataset, focusing on the performance of the Assessing Writing Accuracy task. These experiments involved randomly splitting the dataset into training and testing sets multiple times, calculating the mean and standard deviation across iterations to evaluate the model's stability and generalization capability in the writing accuracy task. Furthermore, we performed statistical analysis on the Monte Carlo results using 95% confidence intervals and \(p\)-values as evaluation metrics. Confidence intervals estimate the plausible range of parameters, reflecting the reproducibility and stability of the experimental results. The 95% confidence level was chosen because it is widely accepted in statistics, striking a balance between confidence level and interval width, ensuring high reliability without overly broad intervals that could obscure meaningful information. \(p\)-values test the statistical significance of the results, determining whether observed differences are due to random chance. Typically, a \(p\)-value less than 0.05 indicates a significant finding. After conducting ten Monte Carlo experiments, the calculated statistical data, including the mean \(\pm\) standard deviation (Mean \(\pm\) SD), 95% confidence interval (95% CI), and \(p\)-value, are presented in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Mean \(\pm\) SD** & **95\% CI** & \(p\)**-Value** \\ \hline Monte Carlo & 98.19 \(\pm\) 0.316 & [97.964, 98.416] & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical significance analysis. In the task of assessing writing accuracy, the proposed MSSE model demonstrated exceptional performance in Monte Carlo experiments, achieving an average accuracy of 98.19% (\(\pm\)0.316). The 95% confidence interval of [97.964, 98.416] indicates that the model's accuracy is highly reliable, with minimal variability across experiments. Additionally, the statistically significant \(p\)-value of 0.002 confirms that the high accuracy is not due to chance but reflects the model's robust performance. These results highlight the consistency and reliability of the MSSE model in handling the complexity and diversity of handwritten Chinese characters within the context of handwriting proficiency examinations.