Character recognition of license plate image based on multiple classifiers

Author

Lin, Bo ; Fang, Bin ; Li, Dong-hui

Author_Institution

Dept. of Comput. Sci., Chongqing Univ., Chongqing, China

fYear

2009

fDate

12-15 July 2009

Firstpage

138

Lastpage

143

Abstract

An approach of character recognition of license plate image based on multiple classifiers is proposed in this paper. For numbers and English characters, features are extracted from binary character images by ET1, DT12 and jumping. For Chinese characters, features are extracted from gray-scale character images by Gabor filters which are specially designed from statistical information. In order to access a high recognition rate, 3 classifiers are used. They are SVM, BP ANN and minimum distance classifier. All the classifiers are arranged by a structure of voting. Experiments show that the proposed method has effective performance on Chinese character image recognition.

Keywords

backpropagation; character recognition; feature extraction; image recognition; neural nets; pattern classification; support vector machines; backpropagation artificial neural networks; character recognition; feature extraction; image recognition; license plate image; multiple classifiers; support vector machines; Character recognition; Data mining; Feature extraction; Gabor filters; Gray-scale; Image recognition; Licenses; Support vector machine classification; Support vector machines; Voting; Gabor filters; License Plate Recognition; Multiple Classifiers;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3728-3

Electronic_ISBN

978-1-4244-3729-0

Type

conf

DOI

10.1109/ICWAPR.2009.5207413

Filename

5207413