A hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

被引:2
作者
Lee, HC [1 ]
Jong, CS [1 ]
机构
[1] Yuan Ze Univ, Dept Informat Management, Tao Yuan 320, Taiwan
来源
APPLICATIONS AND SCIENCE OF COMPUTATIONAL INTELLIGENCE | 1998年 / 3390卷
关键词
pattern recognition; neural networks; vehicle license plate recognition; vehicle identification number; computer vision; SimNet;
D O I
10.1117/12.304802
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most currently available vehicle identification systems use techniques such as R. F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, a novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.
引用
收藏
页码:159 / 168
页数:10
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