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

arXiv: https://arxiv.org/abs/2503.15023 · HTML: https://mlsanity.com/paper/2503.15023/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2025.

## Abstract (arXiv)

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.