# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 5 Discussion and Comparisons  
The primary purpose of this study was to develop a hybrid model that recognizes Arabic handwritten characters accurately. In this section, the performances of the hybrid CNN-based architectures will be discussed from three perspectives: the basic CNN architectures and the ML classifiers utilized in the hybrid models. The Arabic MNIST and Arabic character datasets were used to assess the hybrid models developed in the current study. Ten standalone machine-learning models using the Arabic MNIST dataset, eight standalone machine-learning models trained on the Arabic character dataset, and five hybrid models for both the Arabic MNIST and Arabic character datasets were developed. According to the results, the performances of several CNN-based models for the Arabic character dataset were considerably inferior to the performances for the Arabic MNIST [49] dataset. Therefore, the Arabic character dataset can be considered a more complicated and challenging dataset. In this study, the best performance for the Arabic MNIST dataset was 97% (Figures 9 and 10), achieved with the hybrid model that combined a CNN and an SVM. \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)** & **Accuracy Score** \\ \hline CNN + SVM & 0.87 & 0.87 & 0.87 & 0.872 \\ CNN + Random Forest & 0.78 & 0.78 & 0.87 & 0.804 \\ CNN + AdaBoost & 0.14 & 0.17 & 0.13 & 0.1690 \\ CNN + XGBoost & 0.78 & 0.77 & 0.7990 \\ CNN + Logistic Regression & 0.83 & 0.83 & 0.8560 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Hybrid machine-learning models trained on the Arabic character dataset. Figure 9: The accuracy of the hybrid models on the MNIST dataset. For the Arabic MNIST dataset, the Logistic Regression model and the XGBOOST model outperformed all the models in the research field in this area; the previous record of 99.71% was outperformed by our machine-learning approach. The hybrid models, such as CNN + SVM, reached near accuracy. However, hybrid models are more robust than standalone CNN models. A higher recall rate was observed for the hybrid models. The models which had the worst performances were the AdaBoost and CNN + AdaBoost models. For the Arabic MNIST digit dataset, the machine-learning methods Random Forest, CatBoost, SVM, AdaBoost, XGBoost, and Logistic Regression were employed for model training and testing. In comparison to the other algorithms, XGBoost yielded the best results, while Logistic Regression and Linear SVM also performed quite well. Each of these models performed well. This demonstrates that the models were fine-tuned in accordance with the datasets used. The Arabic MNIST digit dataset is a typical dataset for artificial intelligence research. This implies that it has been standardized to prevent overfitting, indicating that our models are legitimate. However, on the Arabic character dataset, the results obtained were average, with Logistic Regression and the SVM (RBF kernel) achieving the best accuracy (0.65), followed by the SVM (linear kernel). The SVM accuracy dropped as low as 0.43 (with the linear kernel). The machine-learning methods examined were Random Forest, SVM, Neural Network, XGBoost, and Logistic Regression. On the basis of feature extraction in machine learning, the findings for the Arabic character dataset were reviewed, and the SVM (RBF) yielded superior results compared to the other algorithms, although the results were not the greatest overall. In contrast, three deep neural networks were trained and assessed, including a feed-forward network, a convolutional neural network, and a neural network along with transfer learning. These models were evaluated using several optimizers to determine their performances. Experiments performed on our two datasets yielded amazing outcomes.