###### Abstract
Such algorithms require considerable workload and attention and user intervention in each stage. Meanwhile, they require difficult preprocessing algorithms to prepare the Arabic text features. To date, almost all these representation techniques of text classification depend on words rather than character encoding. Simple statistics of some ordered word combinations (e.g., n-grams) usually perform text recognition in a better way [29]. As we mentioned above, researchers who worked in Arabic classification still have some limitations:  
* First, the approach seeks to minimize the Arabic characters' normalization by replacing the extended Arabic alphabets that have similar shapes with the basic one but with slightly different meanings such as \(\uparrow\)/ and \(\uparrow\) with \(\downarrow\) alphabet letter \(\mathfrak{s}\) with \(\mathfrak{s}\), or letter \(\_\)/\(\_\) with \(\_\)/\(\_\). Thus, the normalization process will directly affect the contextual meaning of similar shape words; for example, \(\mathfrak{s}\)/\(\_\) means \"football\", \(\mathfrak{s}\)/ means \"hatted\", \(\_\)/\(\_\) means \"the name of [PERSON]\", \(\_\)/\(\_\) means \"on the thing\", \(\_\)/\(\_\) means \"overflowed\", and \(\_\)/\(\_\) means \"mouse\". In our character-level encoding methodology, we consider the different meanings of all of these characters by encoding them independently. This is to maintain the basic meaning of the different Arabic sentences. * Second, to avoid the stemming problem, we need to understand the word's root during the embedding process. The stemming problem is considered a major challenge for the Arabic language [30]. * Third, we also consider and encode stop words (i.e., \"\(\_\)/\(\_\)embedding process to reduce the textual feature dimensionality, but it affected the understanding accuracy of the Arabic sentences. * Fourth, we solve the problem of alphabet position dependence, which means that the encoding of the Arabic alphabets, based on their positions in a single word, should be different (e.g., at the beginning, middle, or at the end, or ). Such difficulties inspired us to employ the deep learning Arabic text computer-aided recognition (ArCAR) system based on the character level instead of the word or sentence level to easily understand and recognize Arabic for documents as well as Arabic sentiment analysis. The contributions of this work are summarized as follows:  
* The quantization method is used to represent the Arabic text based on the character level. * We have designed a new deep learning ArCAR system for Arabic text classification based on the capability of the deep convolutional network. The key idea of our system is to eliminate the need for Arabic text pre-processing which is very challenging and to achieve reasonable classification results. * The deep learning ArCAR system is proposed to represent and recognize Arabic text in character level for two applications: (1) Arabic text documents recognition and (2) Arabic sentiment analysis. * Finally, we conduct experiments and show the effectiveness of the proposed models for representation and classification to achieve excellent performance in solving many problems such as text recognition at the character level, which is better than at the word level to solve some problems such as out-of-vocabulary problems. This will assist with fast Arabic language understanding or even solving Arabic text recognition problems. To our knowledge, this is the first study to encode all Arabic characters with number and necessary symbols that represent and classify Arabic textual contents considering many characteristics of Arabic text.