A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

2008 ◽  
Vol 22 (02) ◽  
pp. 233-251 ◽  
Author(s):  
YO-PING HUANG ◽  
TSUN-WEI CHANG ◽  
YEN-REN CHEN ◽  
FRODE EIKA SANDNES
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Frames ◽  
Parking Lot

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK

2006 ◽  
Vol 04 (04) ◽  
pp. 601-615 ◽  
Author(s):  
TIAN-DING CHEN
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Structure Modification ◽  
New Approach ◽  
Low Pass

This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for locating a license plate from an image, and has a high tolerance for license plate displacement in the images. Back-Propagation Neural Network (BPNN) has the advantage of anti-noise and anti-distortion, but the problems of traditional BPNN are a longer learning period, iterations are not prone to convergence, and local minimum. The proposed methods combine the parallel distributive process concept with the BPNN structure modification to solve the above problems. This paper also utilizes PPNN to solve taking position, scale and rotation of the license-plate recognition.

scholarly journals License Plate Recognition with Feature Salience and Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2985-2990
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Core Component ◽  
Character Extraction ◽  
Recognition Result

Character recognition algorithm is considered as a core component of License Plate Recognition (LPR) systems. Numerous methods for License Plate (LP) recognition have been developed in recent years. However, most of them are not advanced enough to recognize in complex background and still demand improvement. This paper introduces a novel system for LPR by analyzing vehicle images. Accurate segmentation of license plate and character extraction from the plate is accomplished. In the plate segmentation module, Hough transform is put forwarded to identify plate edges using line segments. Radon transform adjusts the skew between LP and the viewer, thereby improve the recognition result. Four features are extracted from the LP image, and best features are selected using feature-salience theory. Histogram projection is performed horizontally and vertically to isolate individual characters in the LP. Finally, Back Propagation Neural Network (BPNN) is used to identify the characters present in the LP. From experimental results, it is evident that the proposed system can recognize LP more efficiently and establish a good background for future advancements in LPR.

License Plate Recognition Based on Neural Network Algorithm to Improve Research

2013 ◽  
Vol 860-863 ◽  
pp. 2892-2897 ◽  
Author(s):  
De Yong Liu ◽  
Hong Song ◽  
Quan Pan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Recognition Rate ◽  
Training Transfer ◽  
License Plate ◽  
License Plate Recognition ◽  
The Real ◽  
Time Performance

with the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt; Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.

Automatic Car Number Plate Recognition System for Authorization

2017 ◽  
Vol MCSP2017 (01) ◽  
pp. 30-34
Author(s):  
Somalin Sandha ◽  
Debaraj Rana
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Performance Level ◽  
License Plate ◽  
License Plate Recognition ◽  
Human Power ◽  
Automatic License Plate Recognition

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

2013 ◽  
Vol 284-287 ◽  
pp. 2402-2406 ◽  
Author(s):  
Rong Choi Lee ◽  
King Chu Hung ◽  
Huan Sheng Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Recognition Rate ◽  
Low Frequency ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Image Pixels

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

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