# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
###### Abstract  
Automatic handwriting recognition has received considerable attention over the past three decades. Handwriting recognition systems are useful for a wide range of applications. Much research has been conducted to address the problem in Latin languages. However, less research has focused on the Arabic language, especially concerning recognizing children's Arabic handwriting. This task is essential as the demand for educational applications to practice writing and spelling Arabic letters is increasing. Thus, the development of Arabic handwriting recognition systems and applications for children is important. In this paper, we propose two deep learning-based models for the recognition of children's Arabic handwriting. The proposed models, a convolutional neural network (CNN) and a pre-trained CNN (VGG-16) were trained using Hijja, a recent dataset of Arabic children's handwriting collected in Saudi Arabia. We also train and test our proposed models using the Arabic Handwritten Character Dataset (AHCD). We compare the performance of the proposed models with similar models from the literature. The results indicate that our proposed CNN outperforms the pre-trained CNN (VGG-16) and the other compared models from the literature. Moreover, we developed Mutgin, a prototype to help children practice Arabic handwriting. The prototype was evaluated by target users, and the results are reported. automatic handwriting recognition; Arabic handwritten character recognition; machine learning: convolutional neural network; deep learning +
Footnote †: journal: Medical  
(ANN) [2]. Additionally, AHCR models that use convolutional neural networks (CNNs) have achieved outstanding results in a variety of handwritten datasets [2]. CNNs automatically detect and extract images' distinctive and representative features, outperforming traditional ML algorithms that require the features to be manually defined (e.g., SVMs) [2]. In the fields of computer vision, machine vision, speech recognition, natural language processing, and audio recognition, deep learning has achieved excellent success [3; 4; 5]. Recently, deep learning has also demonstrated remarkable results in handwriting recognition. Handwriting-based systems are being employed to help children in their education. Researchers have spent an abundance of time and effort developing and improving handwriting learning systems for schoolchildren in different languages and on different platforms including web pages, computer-based applications and tablet applications [6]. This is encouraged by the fact that writing using digital notebooks allows children to obtain immediate feedback, which improves their learning experience. In addition, it facilitates the personalization of the learning process for better learning outcomes. [PERSON] et al. [7] presented a comparative study in French preschools that focused on handwriting acquisition using digital devices versus paper. The results showed that children trained on digital devices performed significantly better than children trained on paper, particularly in terms of fluency (decreased 'in-air' time and stopping time). Despite the importance and demand for Arabic handwriting recognition systems for children's handwriting, little published research is available [1; 8; 9]. In this paper, we detail the development of deep learning-based models to recognize children's handwriting using the Hijja dataset [1]. This research presents the following contributions to the literature: (1) improved Arabic handwriting recognition models based on CNN. The gap that we address is the need to investigate the recognition of children's handwriting.