A Comparative Study of Four Deep Neural Networks for Automatic License Number Plate Recognition System
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| Natura: | Recurso digital |
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2022
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| _version_ | 1866901344627982336 |
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| author | Ganga Krishnan. G Natheera Beevi M |
| author_facet | Ganga Krishnan. G Natheera Beevi M |
| contents | Numerous aspects of daily life are still being transformed by technologies and services that geared towards intelligent transportation systems and smart automobiles. Automatic Number Plate Recognition has ingrained itself in our culture and is here to stay. The approach used to examine a vehicle's license plate in a photo or video collection is referred to as Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR). Intelligent Transportation Systems are made possible by ANPR technology, which also reduces the need for human interaction. This project aims to find out the best algorithm for license plate detection. The project uses four deep neural networks such as CNN, VGG16, VGG19, and YOLOV3 to detect the license number plate and evaluate the performance of the models in terms of accuracy and find out the best model. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18411137 |
| institution | Zenodo |
| language | |
| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Comparative Study of Four Deep Neural Networks for Automatic License Number Plate Recognition System Ganga Krishnan. G Natheera Beevi M ANPR ALPR CNN VGG16 VGG19 YOLOV3 Numerous aspects of daily life are still being transformed by technologies and services that geared towards intelligent transportation systems and smart automobiles. Automatic Number Plate Recognition has ingrained itself in our culture and is here to stay. The approach used to examine a vehicle's license plate in a photo or video collection is referred to as Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR). Intelligent Transportation Systems are made possible by ANPR technology, which also reduces the need for human interaction. This project aims to find out the best algorithm for license plate detection. The project uses four deep neural networks such as CNN, VGG16, VGG19, and YOLOV3 to detect the license number plate and evaluate the performance of the models in terms of accuracy and find out the best model. |
| title | A Comparative Study of Four Deep Neural Networks for Automatic License Number Plate Recognition System |
| topic | ANPR ALPR CNN VGG16 VGG19 YOLOV3 |
| url | https://doi.org/10.5281/zenodo.18411137 |