Deep convolutional neural network for isolated Arabic handwritten character recognition: design, evaluation, and...

The recognition of handwritten Arabic characters offerings a multifaceted challenge that holds fundamental standing across domains such as document digitization, human-computer interaction, and assistive technologies. Arabic script's cursive form combined with positional character variations and diverse handwriting styles creates substantial obstacles for traditional machine learning techniques. In this study, we propose a deep Convolutional Neural Network (CNN) architecture tailored for the classification of isolated handwritten Arabic letters. The dataset includes 28 classes representing the Arabic alphabet, with balanced samples preprocessed and augmented for robust training. The proposed CNN achieved a high classification accuracy of 96.8%, significantly outperforming Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), which recorded 85.3% and 82.1%, respectively. Performance was evaluated using cross-validation, confusion matrix analysis, and statistical testing. A paired t-test yielded a p-value < 0.01, confirming the statistical significance of the CNN's superiority. This work possesses significant potential for practical deployment in areas including postal address reading bank check processing educational tools and historical manuscript digitization. The model's architectural design allows extension to Persian and Urdu cursive-based languages which enables multilingual handwriting recognition. Future directions include scaling the system to support connected script and word-level recognition as well as integration into mobile or web-based OCR systems for broader accessibility and real-time use. The proposed CNN architecture adheres to traditional deep learning design principles yet demonstrates its unique contribution through specialized application to isolated Arabic handwriting by employing a meticulously balanced dataset alongside an extensive augmentation pipeline and conducting statistically validated comparisons with classical methods. The study also provides a reproducible framework benchmarked on real handwriting variations.