## 4 Results and Discussion
### Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Methods** & **Accuracy** & **Specificity** & **Sensitivity** \\ \hline
**Adaboost** & 82.5 & 78.7 & 84.7 \\
**SVM** & 80.8 & 86.4 & 78.5 \\
**RF** & 78.6 & 85.3 & 74.4 \\
**LSTM** & 90.1 & 91.4 & 91.3 \\
**RPN** & 88.2 & 86.5 & 86.3 \\
**Fast RCNN** & 91.8 & 91.2 & 91.1 \\
**Faster RCNN** & 92.1 & 91.7 & 91.5 \\
**R2 CNN** & 94.2 & 92.1 & 91.5 \\
**NDR-R2 CNN** & 98.2 & 96.4 & 100 \\
**CNN-LSTM** & 92.6 & 92.2 & 93.1 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative results for various models. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Task** & **Accuracy\({}_{uni}\)** & **Accuracy\({}_{norm}\)** & **Accuracy\({}_{mean}\)** & **Specificity** & **Sensitivity** \\ \hline
**1** & 96.0 & 97.0 & 96.5 & 96.9 & 96.2 \\
**1\({}_{max}\)** & 94.8 & 95.2 & 95.0 & 94.8 & 95.1 \\
**1e** & 91.9 & 92.7 & 92.3 & 92.8 & 91.8 \\
**1e\({}_{max}\)** & 90.7 & 91.3 & 91.0 & 90.2 & 91.8 \\
**Leto** & 93.0 & 93.4 & 93.2 & 91.8 & 94.7 \\
**Lamboken** & 93.6 & 94.8 & 94.2 & 94.5 & 93.8 \\
**Hrackarstvo** & 89.7 & 90.6 & 90.1 & 88.6 & 91.6 \\
**Sentence** & 88.8 & 89.6 & 89.2 & 88.6 & 89.8 \\
**All** & 92.3 & 93.0 & 92.6 & 92.2 & 93.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy, specificity, and sensitivity for various tasks. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Task** & **Accuracy\({}_{uni}\)** & **Accuracy\({}_{norm}\)** & **Accuracy\({}_{mean}\)** & **Specificity** & **Sensitivity** \\ \hline
**Stroke** & 94.3 & 95.7 & 95.0 & 94.9 & 95.1 \\
**Shape** & 93.8 & 94.4 & 94.1 & 92.1 & 95.7 \\
**Texture** & 91.6 & 93.8 & 92.7 & 91.4 & 93.8 \\
**Strutural** & 92.8 & 93.4 & 93.1 & 92.4 & 93.8 \\
**Grid** & 94.9 & 96.9 & 95.9 & 96.5 & 95.3 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy, specificity, and sensitivity for various features. There are several challenges to getting results for this work. One possible problem is obtaining a big and varied collection of handwritten characters from dysgraphia patients  
Figure 4: Comparison results of accuracy, specificity and sensitivity for various features. Figure 5: Comparison results of accuracy, specificity and sensitivity for various methods. Figure 3: Comparison results of accuracy, specificity and sensitivity for various tasks. identification performance. Overall, the task's findings show the potential for combining deep learning and machine learning methods to recognize handwritten characters. These results may have significant applications for several uses in the fields of pattern recognition and computer vision. The suggested CNN-LSTM model can be improved and optimized in future studies. One possible approach is to investigate the use of transfer learning methods, which have been demonstrated to be successful in decreasing training time and increasing accuracy in a variety of image recognition tasks. Another direction for future research is to look into using additional features in the feature extraction process, to enhance the model's ability to identify dysgraphic handwriting. Furthermore, the proposed model can be tested on bigger datasets to verify its performance and generalizability. Conceptualization, F.M. ; methodology, F.M. and W.U.K. ; software, W.U.K. ; validation, W.U.K., K.U. and A.K. ; formal analysis, W.U.K. ; investigation, F.M. ; resources, W.U.K. ; data curation, F.M. ; writing--original draft preparation, F.M. ; writing--review and editing, W.U.K., A.K. and F.H.A. ; visualization, W.U.K., K.U. and A.K. ; supervision, F.M., F.H.A., H.A. and H.A. ; project administration, F.M., K.U. and H.A. ; funding acquisition, H.A. All authors have read and agreed to the published version of the manuscript. Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R54), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Not applicable.