Handwritten Arabic script recognition is a challenging task due to the script's dynamic letter forms and contextual variations. This paper proposes a hybrid approach combining convolutional neural networks (CNNs) and Transformer-based architectures to address these complexities. We evaluated custom and fine-tuned models, including EfficientNet-B7 and Vision Transformer (ViT-B16), and introduced an ensemble model that leverages confidence-based fusion to integrate their strengths. Our ensemble achieves remarkable performance on the IFN/ENIT dataset, with 96.38% accuracy for letter classification and 97.22% for positional classification. The results highlight the complementary nature of CNNs and Transformers, demonstrating their combined potential for robust Arabic handwriting recognition. This work advances OCR systems, offering a scalable solution for real-world applications.
@article{arxiv.2503.15023,
title = {Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script},
author = {Chaouki Boufenar and Mehdi Ayoub Rabiai and Boualem Nadjib Zahaf and Khelil Rafik Ouaras},
journal= {arXiv preprint arXiv:2503.15023},
year = {2025}
}