Murshed Dokhan, Sabri Nasser Hussein and Nureize Arbaiy, Nureize Arbaiy (2024) Deep learning development for automatic license plate detection with text extraction capability. In: Applied Information Technology And Computer Science.
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Abstract
Vehicle identification and tracking are essential in traffic surveillance systems, which prioritize safety and efficiency. Security personnel still manually observe medium-sized structures. Security officers physically record license plate numbers after visually examining passing automobiles. This process is time-consuming and error prone. This project aims to create a web-based deep learning system for automated license plate identification with text extraction at UTHM, Malaysia. A variety of car license plates will train the system. A convolutional neural network will immediately detect and interpret license plate text. The user may submit a vehicle photo to the online interface, and the system will recognize the license plate number and return a picture with the highlighted plate. The system will be tested on a set of concepts using accuracy and precision criteria. Traffic management and vehicle identification might employ the created technology
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | Automatic License Plate Detection with Text Extraction Capability |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Faculty of Computer Science and Information Technology > FSKTM |
| Depositing User: | Mrs. Sabarina Che Mat |
| Date Deposited: | 29 Apr 2025 02:39 |
| Last Modified: | 29 Apr 2025 02:40 |
| URI: | http://eprints.uthm.edu.my/id/eprint/12226 |
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