Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916656754720768 |
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| author | Boufenar, Chaouki Rabiai, Mehdi Ayoub Zahaf, Boualem Nadjib Ouaras, Khelil Rafik |
| author_facet | Boufenar, Chaouki Rabiai, Mehdi Ayoub Zahaf, Boualem Nadjib Ouaras, Khelil Rafik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script Boufenar, Chaouki Rabiai, Mehdi Ayoub Zahaf, Boualem Nadjib Ouaras, Khelil Rafik Computer Vision and Pattern Recognition 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. |
| title | Bridging the Gap: Fusing CNNs and Transformers to Decode the Elegance of Handwritten Arabic Script |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15023 |