## 5 Discussion
### Arabic Documents Text Recognition  
For the first application of Arabic documents text classification, the evaluation results show the reliability of the ArCAR system, achieving promising recognition of the best results using the Alarabiya-balance dataset with 97.76% and 94.09% in terms of overall accuracy and F1-score, respectively. Using the AlKhaleej and OSAC datasets, evaluation results of 97.47% and 97.60% in terms of overall accuracies are obtained, respectively. Meanwhile, the evaluation results using Akhbarona and AIDT depict overall accuracies of 96.68% and 96.59%, respectively. The lowest accuracy achieved by this system is recorded using the BBC dataset with 81.63%. As shown in Table 7 and Figures 2 and 3, the proposed ArCAR system shows its capability and reliability to handle different datasets regardless of the number of classes. For example, the ArCAR system performs well to achieve promising results using AIDT with ten different classes, achieving overall accuracy, F1-score, MCC, precision, and NPV results of 96.59%, 90.17%, 88.58%, 90.73%, and 98.18%, respectively. Similarly, the proposed ArCAR system achieves encouraging evaluation results using the OSAC dataset with ten classes. Using the OSAC dataset, it achieves an overall accuracy and F1-score of 97.60% and 91.40%, respectively. In addition to the promising overall accuracy achieved by the proposed ArCAR, a rapid recognition time is also required. As shown in Tables 7 and 8, the proposed ArCAR system presents the capability and feasibility of achieving encouraging and rapid recognition rates for Arabic text documents. As shown in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\begin{tabular}{} **\
\begin{tabular}{} \end{tabular}** \\ **(s)** \\ \end{tabular} & **No. of Epochs** & **
\begin{tabular}{} \end{tabular}** \\ \hline BRAD Binary-class & 1594.02 & 11 & 3.3 \\ BRAD Multiclass & 3511.13 & 10 & 10.76 \\ HARD Binary-class & 93.03 & 10 & 2.44 \\ HARD Multiclass & 648.03 & 12 & 9.83 \\ \hline \hline \end{tabular}  
* The time is computed considering all datasets from all classes.  
\end{table} 
Table 10: Computation Measurements for the Proposed Deep Learning ArCAR System with Different Datasets: Arabic Sentiment Analysis. Table 8, the ArCAR system needs less than 0.264 msec (i.e., for Alkhaleej as an example) to complete the recognition process for each Arabic text document. The proposed remedies of the ArCAR system due to the unbalanced datasets for training and testing help to train the deep learning model in a stable way. In addition, it helps to achieve more stable evaluation results for balanced and unbalanced datasets, as shown in Figure 6. Although some unbalanced training and testing remedies are used, the performance of the proposed ArCAR system is still better with balanced datasets. This is clearly shown with the evaluation F1-score index using the Alarabiya dataset, where it always measures the system evaluation regardless of the imbalance issue in the testing datasets. Unfortunately, other datasets of Alkhaleej, BBC, CNN, OSAC, and AIDT do not have balanced subsets to show the system performance. However, it is clearly shown that using the remedies of the unbalanced datasets, the proposed ArCAR system achieves similar evaluation results in terms of all evaluation indices. Using the balanced and unbalanced HARD datasets, overall accuracies of 93.23% and 93.58%, respectively, are achieved. However, the overall system accuracy using the balanced dataset is slightly better by 4.33% than that using the unbalanced dataset. The direct comparison results using the same datasets with different machine learning classifiers and different Arabic text representation (i.e., word-level and character-level) is presented as in Table 11. Due to the use of many datasets in our work, we compare only two datasets (i.e., unbalanced CNN and Alkhaleej). As is clearly shown in Table 11, our proposed ArCAR achieved comparable and better results compared with the traditional machine learning algorithms.