This study thoroughly investigates how well deep learning models can recognize Arabic handwritten text for person biometric identification. It compares three advanced architectures -- ResNet50, MobileNetV2, and EfficientNetB7 -- using three widely recognized datasets: AHAWP, Khatt, and LAMIS-MSHD. Results show that EfficientNetB7 outperforms the others, achieving test accuracies of 98.57\%, 99.15\%, and 99.79\% on AHAWP, Khatt, and LAMIS-MSHD datasets, respectively. EfficientNetB7's exceptional performance is credited to its innovative techniques, including compound scaling, depth-wise separable convolutions, and squeeze-and-excitation blocks. These features allow the model to extract more abstract and distinctive features from handwritten text images. The study's findings hold significant implications for enhancing identity verification and authentication systems, highlighting the potential of deep learning in Arabic handwritten text recognition for person biometric identification.

翻译：本研究深入探讨了深度学习模型在阿拉伯语手写文本识别用于人员生物特征识别方面的性能。研究使用了三个广泛认可的数据集——AHAWP、Khatt和LAMIS-MSHD，对三种先进架构（ResNet50、MobileNetV2和EfficientNetB7）进行了比较。结果表明，EfficientNetB7表现最优，在AHAWP、Khatt和LAMIS-MSHD数据集上分别达到了98.57%、99.15%和99.79%的测试准确率。EfficientNetB7的卓越性能归功于其创新技术，包括复合缩放、深度可分离卷积和挤压-激励模块。这些特性使模型能够从手写文本图像中提取更抽象和更具区分性的特征。本研究的发现对增强身份验证与认证系统具有重要意义，凸显了深度学习在阿拉伯语手写文本识别用于人员生物特征识别领域的潜力。