Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909214656430080 |
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| author | Balat, Mazen Mohamed, Youssef Heakl, Ahmed Zaky, Ahmed |
| author_facet | Balat, Mazen Mohamed, Youssef Heakl, Ahmed Zaky, Ahmed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00409 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach Balat, Mazen Mohamed, Youssef Heakl, Ahmed Zaky, Ahmed Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Neural and Evolutionary Computing 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. |
| title | Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2406.00409 |