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.

该研究深入探讨了深度学习模型在阿拉伯手写文本的人物生物识别上的准确性，并比较了三种先进的架构——ResNet50、MobileNetV2和EfficientNetB7，使用了三个广泛认可的数据集：AHAWP，Khatt和LAMIS-MSHD。结果表明，EfficientNetB7在AHAWP，Khatt和LAMIS-MSHD数据集上的测试准确率分别达到了98.57％，99.15％和99.79％。EfficientNetB7的出色性能归功于其创新的技术，包括复合缩放、深度可分离卷积和挤压和激励块。研究结果对于增强身份验证和认证系统具有重要意义，突显了深度学习在阿拉伯手写文本识别中的潜力。

基于深度学习的阿拉伯手写文本的个人生物特征识别