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

Autor:	Balat, Mazen, Mohamed, Youssef, Heakl, Ahmed, Zaky, Ahmed
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Multimedia Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 11 figures, 4 tables, International IEEE Conference on the Intelligent Methods, Systems, and Applications (IMSA)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.00409 Zobrazit plný text záznamu View this record from Arxiv