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.
Abstract (translated)
手写阿拉伯文字识别是一项具有挑战性的任务，因为阿拉伯文的字母形式和上下文变化非常动态。本文提出了一种结合卷积神经网络（CNNs）和基于Transformer架构的混合方法来解决这些复杂性问题。我们评估了自定义及微调过的模型，包括EfficientNet-B7和Vision Transformer (ViT-B16)，并引入了一个集成模型，该模型利用基于置信度融合的方法整合它们的优势。我们的集成模型在IFN/ENIT数据集上实现了卓越的性能，字母分类准确率达到了96.38%，位置分类准确率达到97.22%。实验结果突显了CNNs和Transformers之间的互补性，并展示了其结合使用对手写阿拉伯文识别的强大潜力。这项工作促进了OCR系统的进步，为现实世界应用提供了可扩展的解决方案。
URL
https://arxiv.org/abs/2503.15023
