###### Abstract  
A Arabic text classification is a process to simultaneously categorize the different contextual Arabic contents into a proper category. In this paper, a novel deep learning Arabic text computer-aided recognition (ArcCAR) is proposed to represent and recognize Arabic text at the character level. The input Arabic text is quantized in the form of 1D vectors for each Arabic character to represent a 2D array for the ArcCAR system. The ArcAR system is validated over 5-fold cross-validation tests for two applications: Arabic text document classification and Arabic sentiment analysis. For document classification, the ArcAR system achieves the best performance using the Alarabiya-balance dataset in terms of overall accuracy, recall, precision, and F1-score by 97.76%, 94.08%, 94.16%, and 94.09%, respectively. Meanwhile, the ArcAR performs well for Arabic sentiment analysis, achieving the best performance using the hotel Arabic reviews dataset (HARD) balance dataset in terms of overall accuracy and F1-score by 93.58% and 93.23%, respectively. The proposed ArcAR seems to provide a practical solution for accurate Arabic text representation, understanding, and classification. natural language processing (NLP); deep convolutional neural network; Arabic text recognition; Arabic sentiment analysis; Arabic text computer-aided recognition (ArcAR) +
Footnote †: journal:  
+++ ==WARNING: Truncated because of repetitions==  
++  
official language has 28 alphabet letters, all of which are consonants except three, which are long vowels: \"\(\uparrow\)/Alif\", \"\(\_\)/Waw\", and \"\(\_\)/Ya\" [8]. The Arabic words and sentences are written from right to left. Moreover, most Arabic alphabet letters have multiple written forms depending on their positions in the Arabic word. For example, the Arabic letter \"\(\_\)/dh\" could be written in different forms: \"\(\_\)/end of word (disconnected), \"\(\_\)/end of word (connected)\", \"\(\_\)/middle of word, or \"\(\_\)/beginning of word. In addition, diacritical marks (i.e., short vowels) highly contribute to Arabic phonology, altering the shape of the characters. For example, different combination forms of letters \"\(\_\)/Ba could be generated as \"\(\_\)/y\", \"\(\_\)/y\", \"\(\_\)/y\", and \"\(\_\)/y\". This different nature of the Arabic alphabets makes Arabic word embedding and representation a major challenge [13]. Indeed, the Arabic language always faces many challenges, such as stemming, dialects, phonology, orthography, and morphology. The key is to represent the Arabic text in such a way that minimizes such difficulties [14]. Previous research studies for Arabic text classification have used conventional representation techniques such as bag-of-words and word embedding [15]. In [16], [PERSON] et al. presented a supervised machine learning approach for aspect-based sentiment analysis (ABSA) to classify hotels' Arabic reviews. They used different ML classifiers, including naive Bayes, Bayesian networks, decision trees, k-nearest neighbors (KNNs), and support vector machines (SVMs) using different types of representation such as TF-IDF and N-gram. In [14], [PERSON] et al. built a morpho-semantic knowledge graph from Arabic vocalized corpora. These corpora are transformed into rich graph-based representations to store for morphological and Arabic semantic knowledge. In fact, such techniques use classical classification approaches that start with engineering feature extraction, prominent feature selection, and then classification [17]. Recently, the dominant approaches for NLP tasks have been recurrent neural networks (RNNs), in particular LSTMs and convolutional neural networks (CNNs) [2; 18; 19; 20]. Deep learning architectures have recently been proposed to derive a huge number of text deep features for better performance in different fields: text recognition [18; 21; 22], medical image diagnosis [23; 24; 25; 26; 27], and other computer vision techniques [27; 28]. However, deep learning techniques have proven their capabilities for different recognition tasks.