by Wang, J and Yan, W-Q
Abstract:
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%.
Reference:
BP-Neural network for plate number recognition (Wang, J and Yan, W-Q), Chapter in Deep learning and neural networks: Concepts, methodologies, tools, and applications (Khosrow-Pour, K, ed.), IGI Global, 2020.
Bibtex Entry:
@incollection{wang2020bpneuralrecognition,
address = {USA},
author = {Wang, J and Yan, W-Q},
booktitle = {Deep learning and neural networks: Concepts, methodologies, tools, and applications},
editor = {Khosrow-Pour, K},
number = {66},
pages = {1189--1199},
publisher = {IGI Global},
title = {BP-Neural network for plate number recognition},
url = {https://www.igi-global.com/affiliate/weiqi-yan/246808},
url = {https://www.igi-global.com/chapter/bp-neural-network-for-plate-number-recognition/237929},
year = {2020},
abstract = {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%.},
doi = {10.4018/978-1-7998-0414-7.ch066},
isbn = {1799804151},
isbn = {9781799804147},
keyword = {Computers},
}