# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.1 Standalone Machine-Learning Models  
The standalone models are deep neural networks that carry out the entire classification task, from feature extraction to the final detection of patterns and the ultimate classification of images. The first model trained was the XGBoost model, then the random forest model was trained. Next, the CatBoost model was trained for 100 epochs, with a learning rate of 0.1. Then, the logistic regression model was trained with a cost function of the log. Finally, a Support Vector Machine (SVM) was trained, but since the SVM is a non-linear model, two different kernels were used: the RBF and the linear kernel. The results obtained by the standalone machine-learning models broke down into two tables. Table 5 shows the results for the models trained on the Arabic MNIST digit dataset, while Table 6 show the results obtained by the models trained on the Arabic character dataset.  
The proposed system was evaluated on the Arabic MNIST digit dataset. Evaluation measures were calculated against this dataset, with precision, recall, F1-measure, and accuracy calculated for the test dataset. Machine-learning algorithms, named Random Forest, CatBoost, SVM, AdaBoost, XGBoost, and Logistic Regression, were used for the model training and testing on the Arabic MNIST digit dataset. The XGBoost results were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Precision (Macro Average)**} & **Recall (Macro Average)** & **F1-Score (Macro Average)** & **Accuracy Score** \\ \hline
**Random Forest** & 0.99 & 0.99 & 0.99 & 0.98714 \\
**CatBoost** & 0.95 & 0.95 & 0.95 & 0.95476 \\
**SVM (Linear)** & 1.0 & 1.0 & 1.0 & 0.99628 \\
**SVM (RBF)** & 1.0 & 1.0 & 1.0 & 0.99628 \\
**AdaBoost** & & & & 0.69600 \\
**XGBoost** & 1.0 & 1.0 & 1.0 & 1.00000 \\
**Logistic Regression** & 1.0 & 1.0 & 1.0 & 0.99957 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standalone machine-learning models trained on the Arabic MNIST digit dataset.  
the highest compared to the other algorithms, but Logistic Regression and Linear SVM also performed outstandingly. The results shown in Table 5 were remarkable for the dataset.  
As can be seen from Table 5, above, almost all the models performed extremely well. This goes to show that the models were fine-tuned in such a way that they conformed to the dataset used. The Arabic MNIST digit dataset is a standard dataset used for artificial intelligence projects. This means that it has been standardized to avoid overfitting, which indicates that our models are valid; however, as shown in Table 6, the results obtained were average, with the highest being those for Logistic Regression and the SVM (RBF kernel), both of which achieved an accuracy of 0.65. The accuracies were as low as 0.43 for the SVM (linear kernel).  
Table 6 shows the evaluated results against the Arabic character dataset for the following machine-learning algorithms. The machine-learning algorithms were Random Forest, SVM, Neural Network, XGBoost, and Logistic Regression. Based on the feature extraction in the machine learning, the results were evaluated for the Arabic character dataset. SVM (RBF) had better results than the other algorithms, but the overall results were not the best.