Automated Handwriting Analysis for Early Risk Screening of Learning Disabilities in Arabic-Speaking Children Using Convolutional Neural Networks (CNNs)

Handwriting analysis has recently emerged as a modality for the early identification of learning disabilities, as it can reveal characteristic patterns associated with motor control, spatial planning, and cognitive processing. Nevertheless, most existing automated handwriting analysis systems focus primarily on Latin scripts, while Arabic handwriting remains largely unexplored despite its distinct structural and morphological characteristics. This research presents a Convolutional Neural Network (CNN) model for the early risk screening of dysgraphia in the handwriting of Arabic-speaking children. A specialist-annotated dataset comprising 259 handwriting samples from fourth-grade students in Saudi Arabia was collected and preprocessed using a pipeline that included grayscale conversion, Otsu binarization, spatial normalization, and data augmentation. A custom CNN architecture with batch normalization and global average pooling (GAP) was trained with focal loss to address class imbalance. Furthermore, ten handwriting features grounded in prior literature were extracted and evaluated using four classical machine learning classifiers under identical experimental conditions. The proposed CNN model achieved satisfactory performance, reaching 88.8% accuracy, 92.3% balanced accuracy, and 98.1% sensitivity, substantially outperforming feature-based machine learning approaches, whose best balanced accuracy reached 62.6%. These results demonstrate that discriminative patterns in Arabic-speaking children’s handwriting can potentially be learned by deep neural networks. The proposed approach could support the early identification of children showing indicators of potential dysgraphia, assisting educators and specialists in making timely referrals for further assessment.