| Title |
Deep Learning Based License Plate Number Recognition for Smart Cities |
| ID_Doc |
40460 |
| Authors |
Vetriselvi, T; Lydia, EL; Mohanty, SN; Alabdulkreem, E; Al-Otaibi, S; Al-Rasheed, A; Mansour, RF |
| Title |
Deep Learning Based License Plate Number Recognition for Smart Cities |
| Year |
2022 |
| Published |
Cmc-Computers Materials & Continua, 70, 1 |
| DOI |
10.32604/cmc.2022.020110 |
| Abstract |
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods. |
| Author Keywords |
Deep learning; smart city; tesseract; computer vision; vehicle license plate recognition |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000709118000034 |
| WoS Category |
Computer Science, Information Systems; Materials Science, Multidisciplinary |
| Research Area |
Computer Science; Materials Science |
| PDF |
https://file.techscience.com/ueditor/files/cmc/TSP_CMC_70-1/TSP_CMC_20110/TSP_CMC_20110.pdf
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