Title :
The License Plate Recognition System Based on Fuzzy Theory and BP Neural Network
Author :
Li, Li ; Guangli, Feng
Author_Institution :
Dept. of Comput. Sci. & Eng., Henan Institue of Eng., Zhengzhou, China
Abstract :
In different conditions such as light and complex backgrounds, we get some car images, the traditional methods are slow convergence speed and low accuracy. This paper presents a method which applies fuzzy theory to enhance several features of for target. To obtain the license information, we use an improved BP neural network algorithm, by through setting proper numbers of hidden layer of BP network, we can solve the recognition problems of China´s automobile license such as characters kinds, the numbers, and confusing. This method can improve the accuracy and efficiency of car license recognition, and enhance the system robustness.
Keywords :
automobiles; backpropagation; character recognition; fuzzy set theory; image recognition; neural nets; BP neural network; China automobile license; car image; car license recognition; fuzzy theory; hidden layer; license information; license plate recognition system; recognition problem; slow convergence speed; target feature enhancement; Artificial neural networks; Character recognition; Feature extraction; Image color analysis; Image recognition; Licenses; Neurons; BP Neural Network; Fuzzy Theory; Licence Plate Recognition; Multi-feature;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.77