Samsuryadi, Samsuryadi (2023) A Preliminary Study of Vehicle License Plate Detection and Identification. Springer.
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Abstract
In this study, the authors would like to propose vehicle license plate detection and identification using machine learning approaches. The goal of this study is to pave the way for more in-depth research on vehicle license plate detection and identification using machine learning approaches. A license plate is the vehicle’s unique identity that serves as proof of the legitimacy of the vehicle’s operation. It is typically in the form of a plate or other material with specific specifications issued by the police. This plate is installed on each vehicle and contains the area code, registration number, and validity period. This study begins with a review of several related publications, with a focus on license plate detection and identification for each method. The investigation is furthered by identifying and comprehending the benefits of each method. Finally, the authors attempt to propose a vehicle license plate detection and identification model based on the advantages of each method previously discussed. The proposed model is simulated using Python programming. The simple simulation results show a 99% accuracy rate. Based on the simulation results, it is shown that the contribution of this study is that the Faster RCNN-based model is proven to be used for vehicle license plate detection and identification with fair accuracy. This model, however, is still conceptual and needs to be improved. It will be fully tested and discussed in future work.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q1-295 General |
| Divisions: | 09-Faculty of Computer Science > 55101-Informatics (S2) |
| Depositing User: | Dr. Samsuryadi Sahmin |
| Date Deposited: | 03 May 2023 05:50 |
| Last Modified: | 03 May 2023 05:50 |
| URI: | http://repository.unsri.ac.id/id/eprint/99085 |
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