Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach

Abstract

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 h old significant implications for enhancing identity verification a nd authentication systems, highlighting the potential of deep learning in Arabic handwritten text recognition for person biometric identification.