## 4 Results and Discussion
### Result Analysis  
In this section, results for various performance metrics are discussed. First, the kernel size is chosen from 3 to 9, and L1 is kept higher than L2. Table 1 shows that the accuracy fluctuates somewhat when the kernel size changes. Second, it is noticeable that the network's accuracy does not increase when the network is deeper to higher layers. In short, while the depth of convolutional layers has an effect on accuracy, it is not decisive. In other words, increasing the breadth of the convolutional layers is more successful than making the network deeper for the suggested strategy. We discovered that enhancing the number of filters in addition to LSTM units (width) provides the most effective strategy to increase the model's performance. However, it will result in an increase in the number of parameters. To balance these two aspects, we select a network with a width of (64-128-128) and a kernel size of (5-3) for further discussion. The maximum accuracy of the proposed model is 96.1% and 97.6% for the uniform and normal distributions as mentioned in the last row of Table 1.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{14}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{15}\]  
Various evaluation metrics, such as accuracy, specificity, and sensitivity given in Equations (13)-(15) are also used for the performance model evaluation as mentioned in Tables 2 and 3 for different tasks and features. It is clear that the model accuracy for the sentence is low as compared to the other tasks, whereas the highest accuracy is for task 1 for both the uniform and normal distributions. This is because the sentence has a large number of alphabets that are difficult to write whereas the 'I' task is easy to write by the dysgraphia patient. It is noteworthy that the accuracies for both the uniform and normal distributions are almost the same, with the normal distribution having a slightly larger value than the uniform distribution. The proposed CNN-LSTM model is also compared to the other models as mentioned in [48; 49]. Results in Table 4 suggest that the proposed model is at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Kernel Size** & **Width** & **Accuracy\({}_{uni}\)** & **Accuracy\({}_{norm}\)** & **Accuracy\({}_{mean}\)** \\ \hline
5-3 & 16-32-16 & 3 & 90.3\% & 90.7\% & 90.5\% \\
5-3 & 16-32-32 & 3 & 90.6\% & 91.4\% & 90.9\% \\
5-3 & 32-64-32 & 3 & 90.9\% & 91.9\% & 91.4\% \\
5-3 & 32-64-64 & 3 & 91.4\% & 92.3\% & 91.8\% \\
5-3 & 64-128-64 & 3 & 91.7\% & 92.6\% & 92.1\% \\
5-3 & 64-128-128 & 3 & 92.3\% & 92.9\% & 92.6\% \\
5-3 & 128-256-128 & 3 & 93.3\% & 94.6\% & 93.9\% \\
5-3 & 128-256-256 & 3 & 95.1\%rank 2 with a width of (64-128-128) and kernel size of (5-3). It is worth mentioning that greater accuracy may be achieved if the width of the network is increased. Figures 3 and 4 show the comparison results of accuracy, specificity, and sensitivity for various tasks and features. It has been noticed that the task \(l\) has high accuracy due to the easiness of writing while the accuracy for the sentence is low due to the complexity of the text. The accuracy for grid-based features is high as it is based on the position of the characters within a grid or matrix, such as the position of the characters in relation to other characters or the position of the characters within a specific cell of the grid. The accuracy of the texture-based feature is low among all the features as these features are based on the texture of the characters, such as the presence of loops, dots, or other patterns within the characters. Figure 5 represents comparison results for various methods as mentioned in [48,49]. It is noteworthy that the proposed method is the second best among all the other methods.