## 5 Discussion
### Arabic Documents Text Recognition
For Arabic text representation, we have used bag-of-words (BOW), term frequency-inverse document frequency (TFIDF), and character-level representation. To compare the proposed ArCAR system performance with the latest works as well as the conventional machine learning (CML) techniques, we summarized the comparison results regarding the evaluation metrics, as shown in Table 12. Compared with other recent studies and different methodologies, text recognition in Arabic documents at the character level achieves comparable and promising evaluation results with different datasets. This means that the proposed approach seems to be helpful for real practices of Arabic documents classification with a more accurate recognition rate. It is also shown that the proposed system outperforms CML techniques such as multinomial naive Bayes, Logistic Regression and SVC, and linear SVC. In this study, we performed multinomial naive Bayes to show a direct comparison using the same datasets, as presented in Table 12. For example, using the Alkhaleej dataset, the proposed ArCAR system outperforms other deep learning  
Figure 6: Evaluation performance comparison of the proposed ArCAR using the balanced and unbalanced Alarabiya and Akhbarona datasets. models of DL-CNN-GRU and machine learning models of multinomial naive Bayes by 7.47% and 1.47% in terms of overall accuracies, respectively. Thus, the proposed system may help people understand and analyze the Arabic language in a sufficient and desirable way. This also approves the methodology of Arabic character-level representation and recognition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**World-Level and Character-Level Representations**} \\ \cline{2-5}
**Classification** & \multicolumn{2}{c}{**CNN Dataset (Unbalance)**} & \multicolumn{2}{c}{**AlKhaeej Dataset**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Word-Level Representation**} & \multicolumn{2}{c}{**Char Level**} & \multicolumn{2}{c}{**Word-Level Representation**} & \multicolumn{2}{c}{**Char Level**} \\ \cline{2-5}  & **TFIDF** & **BOW** & **Representation** & **TFIDF** & **BOW** & **Representation** \\ \hline MultinomialNB & 64.00 & 88.00 & & 91.00 & 93.00 & \\ BernoulliNB & 61.00 & 61.00 & & 87.00 & 85.00 & \\ LogisticRegression & 90.00 & 91.30 & - & 94.00 & 96.00 & \\ SGDClassifier & 91.20 & 91.00 & & 94.00 & 95.00 & \\ Support Vector & 90.00 & 90.00 & & 95.00 & 96.00 & \\ Classifier(SVC) & 91.00 & 91.00 & & 94.00 & 96.00 & \\ Linear SVC & 91.00 & 91.00 & & 94.00 & 96.00 & \\ Proposed ArCAR & - & - & 91.56 & - & - & 97.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison Evaluation Results using Different Conventional Machine Learning Classifiers with different Representation Techniques. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Precision/PPV** & **Recall/SE** & **F1-Score** & **Accuracy** \\ \hline \multirow{4}{*}{AlKhaeej} & DL-CNN-GRU & & & & \\  & [PERSON] et al. [9] & - & - & - & 96.0 \\  & Multinomial Naive Bays & 90.0 & 90.0 & 90.0 & 90.0 \\  & Our Conventional & 92.75 & 92.64 & 92.63 & 97.47 \\  & DL-CNN-GRU & - & - & - & 94 \\ [PERSON] & [PERSON] et al. [9] & 89.16 & 88.99 & 88.98 & 96.43 \\  & DL-CNN-GRU & - & - & - & 97.0 \\ [PERSON] & [PERSON] et al. [9] & 94.16 & 94.08 & 94.09 & 97.76 \\  & The proposed ArCAR System & 64.0 & 37.0 & 42.0 & 74.0 \\ BBC & Our Conventional & 71.18 & 69.02 & 69.62 & 81.63 \\  & The proposed ArCAR System & 60.0 & 61.0 & 62.0 & - \\ CNN & Multinomial Naive Bays & 77.0 & 67.0 & 75.43 & 91.0 \\  & Our Conventional & 77.46 & 74.72 & 75.43 & 91.56 \\  & Multi-Layer Perceptron & 91.0 & 90.0 & 90.0 & - \\ OSAC & [PERSON] et al. [43] & & & & \\  & Multinomial Naive Bays & 97.0 & 88.0 & 89.0 & 87.0 \\  & Our Conventional & 93.10 & 91.26 & 91.40 & 97.62 \\  & BERT model & 86 & - & - & - \\ AIDT & [PERSON] et al. [52] & & 90.15 & 97.11 & 90.17 & 96.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison Evaluation Results with the Latest Works for Arabic Text Documents Recognition.