# Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy
## 4 Conclusions and Further Research  
The MSSE model proposed in this study integrates an attention bottleneck layer with a multi-scale fusion module, enabling robust feature extraction and adaptive feature weighting for assessing writing accuracy. Experimental results demonstrate that the model achieves outstanding performance on a self-constructed dataset comprising 26,800 samples from 240 writers, with an accuracy of 98.6%, an \(F1\) score of 95.7%, a recall of 96.9%, and a Kappa coefficient of 94.3%. These metrics highlight the model's strong learning capability and generalization potential. Furthermore, Monte Carlo experiments reveal a mean  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Ours** & **Expert A** & **Expert B** & **Expert C** & **Expert D** \\ \hline Figure 7a & ‘\#’ & ‘\#’ & null & null & null’ \\ Figure 7b & ‘\#’ & ‘\#’ & ‘\#’ & ‘\#’ & ‘\#’ \\ Figure 7c & ‘\#’ & null & ‘\#’ & null & ‘\#’ \\ Figure 7d & ‘\#’ & ‘\#’ & ‘\#’ & ‘\#’ & ‘\#’ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Subjective and objective comparisons.  
Figure 7: Heatmap of some typical images: an example of successful recognition with missing strokes (**a**), Scribbled but successfully recognized (**b**), More hyphenated but successfully recognized (**c**), Only one stroke in the upper part but successfully recognized (**d**), and their respective label and recognition results.  
accuracy of 98.19% (\(\pm\)0.316%) and a 95% confidence interval of [97.964%, 98.416%], with a statistically significant p-value of 0.002, confirming the model's stability and reliability.  
The attention bottleneck layer enhances network depth and feature discriminability, ensuring high accuracy even in complex scenarios. Meanwhile, the multi-scale fusion module adapts to varying feature scales, making it particularly effective for both simple and complex characters in writing grade examinations. Ablation studies further validate the contributions of each component, with the MSSE model achieving a 2.06% improvement in accuracy over the basic version.  
In addition to its performance on the self-constructed dataset, the MSSE model demonstrates excellent generalization on the CASIA-HWDB dataset, achieving an accuracy of 97.05%, which surpasses several state-of-the-art methods. This indicates its potential for practical applications in real-world scenarios. Moreover, the model's architecture allows for further compression and optimization, making it suitable for deployment in resource-constrained environments.  
In summary, the MSSE model not only achieves high recognition accuracy but also exhibits strong robustness and generalization capabilities, making it a promising solution for HCCR tasks in both academic and practical settings.  
While the MSSE model demonstrates strong performance on the Shanghai Writing Grade Examination dataset, its limitations must be acknowledged. The dataset, comprising 26,800 samples from 240 fifth-grade students, is sufficient for initial validation but lacks diversity in terms of age groups, educational backgrounds, and regional variations. This restricts the model's generalizability to broader assessing writing accuracy applications, such as adult handwriting calligraphy recognition or historical document analysis. Future work should focus on expanding the dataset to include more diverse populations and handwriting scenarios. Additionally, the model's computational requirements, driven by its multi-scale fusion module and attention bottleneck layer, may hinder deployment in resource-constrained environments. Techniques such as quantization, pruning, or knowledge distillation should be explored to optimize the model's size and inference speed.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to the use of external datasets as the input data for creating an extended dataset.  
The authors declare no conflicts of interest.