Explainable Deep Learning Approaches for Dyslexia Detection in English and Arabic Handwriting Using Convolutional Neural Networks and Transfer Learning

Dyslexia impacts 5–15% of school-aged children globally, but automated screening mechanisms to detect it are rare, and such tools are relatively scarce in non-Latin scripts. The work introduces a bilingual deep learning model for dyslexia preliminary diagnosis through digitalized handwriting samples in both English and Arabic. Two computational methods were employed and compared systematically: the page-oriented classification strategy and the character-oriented classification method. For Arabic, an EnhancedCNN architecture is proposed to classify whole-page scans end-to-end by coping with cursive script and contextual letter forms. Both a baseline SimpleCNN model and a MobileNetV3-Small transfer learning model were trained on segmented letter crops from 123,554 labeled English samples. Preprocessing steps included the removal of instructor annotations, the Otsu adaptive thresholding method binarization and morphological processing noise removal and stroke refinement. Grad-CAM visualizations were included for model transparency and education decision aids, showing discriminative regions in page-level as well as character-level predictions. Experimental results proved that the proposed Arabic page-level model obtained 77% test accuracy, which constitutes preliminary proof of concept for AI-driven dyslexia screening in Arabic. English character-level approach using MobileNetV3 achieved 99% accuracy on the single letter detection task. This work also contributes to one of the earliest AI-assisted reading screening systems which is specifically designed for detecting dyslexia in Arabic script and brings systematic evidence on comparing hybrid page- and letter-level strategies for bilingual handwriting analysis.