# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
## 1 Introduction  
Arabic is a widely spoken language, with over 360 million people using it as their primary language [1]. In the domain of language processing and technological applications, the recognition of handwritten Arabic characters, especially in the realm of children's script, poses unique challenges [2, 3]. Arabic, being a Semitic language, presents inherent complexities in its script, demanding advanced algorithms for precise recognition. These challenges emanate from factors such as the semi-cursive nature of Arabic writing, distinct character shapes based on their position in a word, and the presence of diacritical marks representing short vowels and other phonetic features. The significance of addressing the issue of Arabic handwritten recognition, particularly concerning children, arises from the increasing integration of technology in their educational and recreational activities. The ubiquitous use of smartphones and tablet devices by children, employing touchscreens and styluses for various purposes, including handwriting, underscores the need for automated recognition techniques tailored to the unique characteristics of children's handwriting [4, 5, 6]. While the existing literature has made notable strides in Arabic handwritten recognition, the focus has predominantly been on adult handwriting. Researchers have exploreddiverse datasets such as the Arabic Handwritten Characters Dataset (AHCD) [7] and the Database of Arabic Handwritten Characters and Ligature (DBAHCL) [8], achieving commendable accuracy rates using both conventional methods (e.g., Support Vector Machine (SVM), K-Nearest Neighbour (KNN)) and advanced techniques (e.g., Convolutional Neural Network (CNN) and Artificial Neural Network (ANN)). Notably, CNNs [9; 10] have emerged as powerful tools for feature extraction, demonstrating superior performance compared to traditional machine learning approaches. In the context of children's handwriting recognition, the existing Hijia dataset [4; 11] is the only resource facilitating the training of deep learning classification models. However, children's handwriting introduces additional complexities, including variations in writing quality, increased variances, and more substantial distortions. Recognizing these distinctions is imperative for developing effective applications in education, interactive learning, and other practical domains tailored to children. Limited research has been conducted in recognizing children's written Arabic characters using the Hijia dataset. The existing literature uses conventional and deep learning approaches towards the classification of children written Arabic characters. [PERSON] and [PERSON] [4] introduced the unique Hijia dataset, training a CNN model with 88% accuracy, but lacked detailed investigations into existing powerful models. [PERSON] et al. [12] implemented a custom CNN model, achieving 92.5% accuracy on the Hijia dataset. [PERSON] et al. [13] introduced the Optimized Leaky Rectified Linear Unit (OLReLU)-CNN model, attaining 90% accuracy on Hijia. [PERSON] and [PERSON] [5] explored custom CNN models, emphasizing the impact of diverse training datasets and achieving an impressive average accuracy of 92.78% on recognizing children's handwritten characters, while also proposing supplementary features for enhanced discrimination. [PERSON] et al. [14] tailored a custom CNN for achieving 91% accuracy on Hijia. Recently, [PERSON] et al. [15] implemented a custom CNN and pre-trained VGG16 models, reporting a remarkable 99% accuracy on the Hijia dataset and introducing the Mutgin application for children's practice. These studies collectively highlight the evolving landscape of deep learning applications in recognizing children's Arabic handwriting. However, concerns about model generalization and sensitivity persist, and further exploration is warranted in this context. This paper introduces a new dataset, \"Dhad\", following procedures similar to those for Hijia to ensure consistency.