# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.2 Standalone Deep-Learning Models  
The second phase of the research consisted of the experiments on the standalone deep neural networks which performed the tasks of feature extraction and classification. Three deep neural networks were trained and evaluated, including the feed-forward network, the convolutional neural network, and the neural network integrated with transfer learning. These models were tested with different optimizers to see how they performed, and a summary of the results obtained is presented in Table 7.  
Figure 6 shows the loss- and accuracy-curve graphs for the feed-forward network, for which the variation was very low after the 10 th iteration. The purpose was to check the variation in the iterations, so that more iterations were required. The variational changes after the 10 th iteration were only minor.  
Figure 7 shows the curves for both the loss and accuracy of the training and validation; the model had an almost perfect training accuracy but a more realistic validation accuracy, which goes to show that the model performed well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision (Macro** & **Recall (Macro** & **F1-Score (Macro** & **Accuracy Score** \\  & **Average)** & **Average)** & **Average)** \\ \hline
**Random Forest** & 0.64 & 0.64 & 0.63 & 0.6336 \\
**CatBoost** & 0.47 & 0.47 & 0.46 & 0.4643 \\
**SVM (Linear)** & 0.41 & 0.41 & 0.41 & 0.4233 \\
**SVM (RBF)** & 0.66 & 0.65 & 0.65 & 0.6529 \\
**Feed-Forward Neural Network (Adam)** & 0.66 & 0.61 & 0.63 & 0.6101 \\
**XGBoost** & 0.49 & 0.49 & 0.48 & 0.4854 \\
**Logistic Regression** & 0.66 & 0.65 & 0.65 & 0.6529 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standalone machine-earning models trained on the Arabic character dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision (Macro** & **Recall (Macro** & **F1-Score (Macro** & **Accuracy Score** \\  & **Average)** & **Average)** & **Accuracy)** \\ \hline
**Feed-Forward Neural Network** & & & & \\
**(Adam)** & 1.0 & 1.0 & 1.0 & 0.98714 \\
**CNN** & 1.0 & 1.0 & 1.0 & 0.99063 \\
**Transfer Learning** & 1.0 & 1.0 & 1.0 & 0.99673 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Standalone deep-learning models trained on the Arabic MNIST digit dataset.  
The loss and accuracy curves for the convolutional neural network can be seen in Figure 7. The model encountered a few problems at the beginning of the experiment, which caused it to move rapidly; however, with more epochs, the line smoothed out, which signifies the model's effectiveness. Figure 8 shows the confusion matrix of the CNN classifier. Variations in the graphs show significant changes in the loss and accuracy for the training and validation. This impact was seen due to the significant change in the dataset. During training, the changes in loss and validation for the loss and accuracy were clearly seen.  
The results obtained for the experiments conducted on our two datasets were impressive. The MNIST dataset was used to train the feed-forward network, the CNN, and the transfer-learning models, which achieved 0.9871, 0.9906, and 0.9967 accuracies, respectively. These are very effective accuracies, and it can be confidently said that the models can be used. On the other hand, the Arabic character dataset was used to train only the CNN with an adagrad optimizer, and it achieved an accuracy of 0.8992, see Table 8, which is not as high as that of MNIST but still remarkably high.  
Figure 6: Graphs of the loss and accuracy curves for the feed-forward network.  
Figure 7: Graphs of loss and accuracy curves for the CNN.