A New DCT-PCM Method for License Plate Number Detection in Drone Images

2021 ◽  
Author(s):  
Hamam Mokayed ◽  
Palaiahnakote Shivakumara ◽  
Hon Hock Woon ◽  
Mohan Kankanhalli ◽  
Tong Lu ◽  
...  
Keyword(s):  
License Plate ◽  
Plate Number

scholarly journals UHF RFID Tag Antenna for Vehicle License Plate Number (e-Plate)

2013 ◽  
Vol 11 (2) ◽  
pp. 337 ◽  
Author(s):  
Evizal Evizal ◽  
Tharek Abdul Rahman ◽  
Sharul Kamal Abdul Rahim
Keyword(s):  
License Plate ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Plate Number

BP-Neural Network for Plate Number Recognition

2016 ◽  
Vol 8 (3) ◽  
pp. 34-45 ◽  
Author(s):  
Jia Wang ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
License Plate ◽  
License Plate Recognition ◽  
Traffic Surveillance ◽  
Plate Number ◽  
Number Recognition ◽  
Traffic Systems ◽  
Artificial Neural Network Ann

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

scholarly journals Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

2020 ◽  
Vol 8 (4) ◽  
pp. 304-310
Author(s):  
Windra Swastika ◽  
Ekky Rino Fajar Sakti ◽  
Mochamad Subianto
Keyword(s):  
High Resolution ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Super Resolution ◽  
Training Data ◽  
License Plate ◽  
Plate Number ◽  
Average Accuracy ◽  
Number Recognition ◽  
High Resolution Images

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

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