DocumentCode :
2954425
Title :
New License Plate Character Recognition Algorithm Based on ICM
Author :
Wu, Jun ; Xiao, ZhiTao
Author_Institution :
Sch. of Inf. & Commun. Eng., Tianjin Polytech. Univ., Tianjin, China
fYear :
2011
fDate :
30-31 July 2011
Firstpage :
1
Lastpage :
4
Abstract :
For License Plate Recognition (LPR) system, license plate character recognition rate is seriously affected by image quality. To resolve this problem, a new algorithm is proposed, in which Intersecting Cortical Model (ICM) is applied into license plate character recognition. ICM was derived from several visual cortex models, which can be applied to image feature extraction efficiently. In the new algorithm ICM was utilized to extract three features from size-normalized binary input character image. Based on these features, weighted voting was performed and final estimation of input character was made. Experiment results show that compared with common algorithms based on BP network, the new ICM based algorithm has higher recognition rate and stronger robustness, and is more convenient and flexible.
Keywords :
backpropagation; character recognition; feature extraction; neural nets; traffic engineering computing; BP network; LPR system; feature extraction; image quality; intersecting cortical model; license plate character recognition rate; size-normalized binary input character image; visual cortex model; weighted voting; Brain modeling; Character recognition; Feature extraction; Image recognition; Licenses; Neurons; Noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control, Automation and Systems Engineering (CASE), 2011 International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0859-6
Type :
conf
DOI :
10.1109/ICCASE.2011.5997683
Filename :
5997683
Link To Document :
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