CFP last date
20 December 2024
Reseach Article

A Review of License Plate Detection and Recognition Techniques

Published on May 2012 by Anju, Sumit Budhiraja
National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
Foundation of Computer Science USA
RTMC - Number 13
May 2012
Authors: Anju, Sumit Budhiraja
b950b266-0aa9-415f-8bc2-d76ddf923856

Anju, Sumit Budhiraja . A Review of License Plate Detection and Recognition Techniques. National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011. RTMC, 13 (May 2012), 11-15.

@article{
author = { Anju, Sumit Budhiraja },
title = { A Review of License Plate Detection and Recognition Techniques },
journal = { National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011 },
issue_date = { May 2012 },
volume = { RTMC },
number = { 13 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 11-15 },
numpages = 5,
url = { /proceedings/rtmc/number13/6716-1115/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%A Anju
%A Sumit Budhiraja
%T A Review of License Plate Detection and Recognition Techniques
%J National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%@ 0975-8887
%V RTMC
%N 13
%P 11-15
%D 2012
%I International Journal of Computer Applications
Abstract

Vehicle license plate detection and recognition is one of the active research areas now a day. There are two main stages in most license plate character recognition systems: features extraction and classification. There are several methods for detection of license plates in various conditions. Features can be extracted through various methods. Artificial Neural Networks are the most popular classification methods used for character recognition systems. In this paper, various techniques for detection and recognition of license plates are discussed.

References
  1. Ming-Kan Wu, ling-Siang Wei, Hao-Chung Shih, and Chian, " License Plate Detection Based on 2-Level 2D Haar Wavelet Transform and Edge Density Verification" IEEE International Symposium on Industrial Electronics ,2009
  2. Kuo-Ming Hung, Hsiang-Lin Chuang, Ching-Tang Hsieh, License Plate Detection Based on Expanded Haar Wavelet transform, Fourth International Conference on Fuzzy Systems and Knowledge Discovery,IEEE,2007
  3. Chirag N. Paunwala, Suprava Patnaik A Novel Multiple License Plate Extraction Technique for Complex Background in Indian Traffic Conditions International Journal of Image Processing (IJIP) Volume (4)
  4. Guangmin SUN, Canhui Zhang, Weiwei ZOU,Guangyu YU,"A New Recognition Method of Vehicle License Plate Based on Genetic Neural Network", 5th IEEE Conference on Industrial Electronics and Applicationsis, pp. 1662-1666,2010
  5. BO LIN, BIN FANG, DONG-HUI LI, "Character recognition of license plate image based on multi-classifiers", IEEE International Conference on wavelet analysis and pattern recognition, pp. 138-143,2009
  6. Youfu Wu, Yongwu, GangZhou, Jing, Wu "Recognizing Characters Based on Gaussian-Hermite Moments and BP Neural Networks", IEEE International conference on intelligent computation technology and automation pp. 992-995, 2010
  7. Zhiwen WANG, Shaozi LI, "Research and Implement for Vehicle License Plate Recognition Based on improved BP Network", IEEE International conference on computer and communication technologies in agriculture engineering, pp101-104, 2010
  8. Baoming Shan, "License Plate Character Segmentation and Recognition Based on RBF Neural Network", IEEE second inter national workshop on education technology and comp uter science pp. 86-89, 2010
  9. Vijaya Laxmi , B. M. Karan " Vehicle Identification Automation Using Wavelet-Probabilistic Neural Network Combined Approach", International Journal of Signal and Image Processing (Vol. 1)pp 62-68
Index Terms

Computer Science
Information Sciences

Keywords

License Plate Detection License Plate Recognition Feature Extraction Neural Networks