# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 1 Introduction  
Despite massive technological advances, many people's textual compositions are still handwritten. Using pen and paper for writing is essential to people's work. Handwriting has different sizes and styles, making the creation of automatic techniques for recognizing texts a challenging task in computer vision [1]. Text-recognition systems utilize automated techniques for text recognition by converting text included in images into matching digital formats. Such systems can discover typed or handwritten characters and are used in different application domains. A handwritten-character-recognition system is a computer-vision system that is intended to classify and recognize handwritten characters [2]. Character recognition is still critical and challenging in many computer-vision tasks [3]. With the increased interest in handwriting recognition and the developments in machine-learning and deep-learning algorithms, researchers have made significant improvements and advances in this field. English-handwriting-recognition methodologies have received significant interest from researchers [4]; however, Arabic has not yet received enough interest. With more than 315 million native Arabic speakers [5], the need for Arabic-handwriting-recognition systems is critical. Arabic is one of the most popular spoken languages in the world, with twenty-eight alphabets and different letter styles, based on geography. In general, handwriting is a pattern-recognition research area with different applications. For each application domain, specific constraints should be considered [6], otherwise the recognition process will be complicated due to the wide range of handwriting styles and sizes. For example, recognizing characters on car license plates is more straightforward than recognizing Arabic handwriting due to the different styles of handwriting. Therefore, researchers are making great efforts to improve recognition systems using various techniques, deep-learning algorithms being at the top of the list. For a while, Arabic handwritten character recognition (AHCR) has been an area of research in pattern recognition and computer vision. Several machine-learning (ML) algorithms, such as support vector machines (SVMs), have improved AHCR. Such models are still limited and cannot outperform convolutional neural networks (CNNs) on different Arabic handwriting datasets. Several Arabic handwriting datasets have been used in the literature to create convolutional neural network (CNN) models [1; 7; 8] that automatically extract features from images and outperform classical machine-learning techniques (such as [9; 10]), especially when large datasets with a large number of classes are used. As [PERSON] and [PERSON] claim [11], better classification results can be achieved when an SVM is replaced with an MLP in deep learning. This is because MLPs are based on empirical risk minimization, which tries to minimize errors in the training set. As a result, the training procedure is terminated when the back-propagation algorithm finds the first separating hyperplane. Several deep-learning architectures have been used in the literature in different applications (deep neural networks (DNNs), convolutional neural networks (CNNs), deep belief network (DBNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs)) [12]. In this work, a CNN, a commonly used deep-learning algorithm, was used to recognize Arabic handwritten characters. Despite all the progress that has been made, there are still some challenges and a demand for new methods to overcome these limitations [13]. Compared to the English language, the quantity of available datasets of Arabic handwritten characters is relatively small. Additionally, some of the published datasets include few records. There is a requirement for a vast dataset containing a variety of font sizes, styles, illuminations, users, and texts [14]. Some of the collected records (pictures) may have unnecessary data noise that must be eliminated, or misclassification might occur.