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arXiv · 2503.15023

Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script

Chaouki Boufenar·Mehdi Ayoub Rabiai·Boualem Nadjib Zahaf·Khelil Rafik Ouaras
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Abstract

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.

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Chaouki Boufenar, Mehdi Ayoub Rabiai, Boualem Nadjib Zahaf, Khelil Rafik Ouaras. 2025-03-19. Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script. https://arxiv.org/abs/2503.15023

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