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The Research on Character Recognition of Vehicles’ License Plates Based on Neural Net Integration System

Author: YeFeng
Tutor: FangMin
School: Hefei University of Technology
Course: Control Theory and Control Engineering
Keywords: Neural network pattern recognition Binarization Feature Extraction Integrated recognition License Plate Recognition System
CLC: TP183
Type: Master's thesis
Year: 2002
Downloads: 368
Quote: 9
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Abstract


The neural network pattern recognition is an important research direction for the rise in recent years, the field of pattern recognition. Compared with the traditional pattern recognition, neural network pattern recognition method demonstrated strong learning ability of self-organization, fault tolerance, robustness, and nonlinear processing advantages, so it has been widely used in various fields. The automatic recognition of the vehicle license is an important application of computer vision and pattern recognition technology in the field of intelligent transportation is an important part of the traffic management. With the increasing levels of automation, intelligent traffic management and monitoring system, the accuracy of license plate recognition system constantly new requirements, so both organic combination of certain theoretical significance and practical value. This article select neural network pattern recognition technology as a method of identification, as the license plate character recognition object, further study of the issue of license plate recognition in case of interference, the license plate recognition system as a whole in order to improve the ability to identify. The papers related to the following work: text image binarization based Application of SOFM (self-organizing feature map network) network gray image binarization method and color image binarization method. Select coarse grid features and directional line element feature as a character recognition feature, and coarse grid characteristics improved SOFM training algorithm has been optimized. This optimization algorithm, the recognition of the Chinese characters of numbers, letters, and provinces, respectively, using the above two features as the SOFM single input classifier. 3, multi-feature input, Neural networks ensemble character recognition method, Bp network classifier SOFM network classifier combined to construct an integrated multi-network classifier. 4, on the basis of theoretical studies, this paper, Visual C 6.0 programming language implementation of the corresponding algorithm constructs a license plate recognition system software platform. This study shows that: input multi-feature integration network classifier, compared with a single classifier can effectively improve the noise immunity and recognition rate, while the comprehensive integrated identification method generally with the noise of the license plate characters recognition system reference design, the reference value.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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