## 4 Results
### Arabic Documents Text Recognition  
Table 7 shows the average evaluation results of the proposed ArCAR system for Arabic documents text classification. These results are derived as an average over 5-fold test using only the testing datasets. These results show the reliability and capability of the proposed ArCAR system, achieving promising evaluation results with different datasets. The overall classification performance using the AlKhaleej dataset was 92.64%, 98.28%, 97.47%, and 92.63% in terms of sensitivity, specificity, accuracy, and F1-score, respectively. Similar encouraging results were obtained using all the reset datasets. All evaluation metrics were derived using the averaged multiclass confusion matrix over a 5-fold test. Figures 2 and 3 show the averaged multiclass confusion matrices for Arabic documents text recognition over each dataset. The sum of each row in the confusion matrix represents the testing set for each class. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DATASET** & **SE** & **SP** & **Az** & **F1-Score** & **MCC** & **PPV** & **NPV** \\ \hline AlKhaleej & 92.64 & 98.28 & 97.47 & 92.63 & 91.55 & 92.75 & 98.28 \\ Abla & 88.99 & 97.67 & 96.43 & 88.98 & 78.31 & 89.16 & 97.67 \\ Akhbarona & 88.08 & 97.93 & 96.68 & 88.28 & 86.55 & 88.60 & 97.94 \\ mul balance & 94.08 & 98.50 & 97.76 & 94.09 & 93.01 & 94.16 & 98.50 \\ Alarabiya & 83.80 & 95.88 & 94.43 & 76.87 & 74.40 & 73.01 & 96.69 \\ unbalance & 69.02 & 85.44 & 81.63 & 69.62 & 57.43 & 71.18 & 83.43 \\ CNN & 74.72 & 94.58 & 91.56 & 75.43 & 70.72 & 77.46 & 84.86 \\ OSAC & 91.26 & 98.49 & 97.60 & 91.40 & 90.85 & 93.10 & 98.39 \\ AIDT & 90.15 & 97.11 & 96.59 & 90.17 & 88.58 & 90.73 & 98.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation Result of the proposed ArCAD system for Arabic documents text classification as an average over 5-fold test. Figure 2: Averaged multiclass confusion matrices of Arabic document text recognition for different datasets with corresponding classes: (**a**) AlKhaleej, (**b**) Akhbarona-unbalance, (**c**) Akhbarona-balance, (**d**) Alarabiya-unbalance, and (**e**) Alarabiya-balance. Moreover, the ArcAR system performance in terms of computation training and testing cost is recorded corresponding to each dataset and listed in Table 8. The computation time for deep learning models depends on the deep structure, learning settings (epochs, batch size, etc. ), the size of the training set, and specifications of the PC. The deep learning structure affects the training and testing time prediction cost. This is because the number of trainable parameters is proportionally increased with the depth of the deep learning model, making the prediction cost high [18; 19]. Thus, accurate and rapid predictions are required for more reliable and feasible text recognition systems. In this study, the proposed ArcAR system has a reasonable depth and achieves promising recognition results in terms of Arabic document recognition and Arabic sentiment analysis. The number of trainable parameters (weights and baises) of the proposed model are recorded to be 11.30 million. For training, an example of the training ArcAR system in terms of training-validation accuracy and loss function behaviors is shown in Figure 4. It is clearly shown that the ArcAR system is trained well, achieving good behaviors without any bias or overfitting. As shown in Table 8, the training and testing processing times are recorded based on the total number of training and testing datasets for all classes with respect to the specific dataset. The training processing time is recorded for each epoch. Indeed, the variation in the dataset sizes directly affects the required training and testing times. This means that the number of Arabic documents in the training set affects the required training time to finalize the learning processes and trainable parameter optimization.