DocumentCode :
1931377
Title :
Automatic vehicle license plate recognition system used in expressway toll collection
Author :
Wenjie, Huang
Author_Institution :
Transp. Coll., Huaiyin Inst. of Technol., Huai´´an, China
Volume :
6
fYear :
2010
fDate :
9-11 July 2010
Firstpage :
159
Lastpage :
162
Abstract :
Based on the binary image, the thesis proposes a suit of algorithm for the license plate intelligent recognition system serving the expressway toll collection system. Firstly, the algorithm of first difference combined with the method of determining the threshold of binarization via accumulating pixel spots is used in pre-processing. Secondly, taking advantage of the horizontal texture characteristic and the physical trait in the plate area, the algorithm locates the position of the plate. Then the positions of left and right border of Chinese character used to division are marked in character segmentation, so that the strokes of Chinese characters cannot degenerate noise. At the end, the algorithm designs different methods of recognition, the enhanced method of BP neural network is for the Chinese characters, and the method of dynamic clustering for Arabic numbers and alphabet letters.
Keywords :
character recognition; image enhancement; image recognition; image resolution; image segmentation; image texture; Arabic numbers; BP neural network; Chinese characters; alphabet letters; automatic vehicle license plate recognition system; binarization threshold; character segmentation; dynamic clustering; expressway toll collection; intelligent recognition system; Feature extraction; Image segmentation; Pixel; Random access memory; character separation; dynamic clustering; extracting characteristic; license plate recognition; neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5537-9
Type :
conf
DOI :
10.1109/ICCSIT.2010.5563718
Filename :
5563718
Link To Document :
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