Abstract
In this study, a new method for using LPR systems for Iranian plates number has been presented. Increasing the precision of the letter recognition process and reducing the amount of training are in fact the main advantages of the new hybrid model. The K-NN has been implemented as the first classification method, because it was simple, and it was resistant to the noisy data, and for large datasets it is also effective at zero cost. The confusion problem related to the similarity of letters in plate numbers has also been resolved by using the classification model of the multi-class genetic algorithm. The genetic algorithm improves K-NN performance in the recognition of similar letters. Vehicle license plate recognition (LPR) plays an important role in ITS and is mainly used in access control systems.The purpose of this research is to determine the Iranian plate automobiles that are specifically owned by the automobile. The confusion caused by the similarity between the letters of the alphabet and numeric characters is one of the problems of the Persian LPR systems at the diagnostic stage. In this regard, a method using the KNN-based advantages of genetic algorithm as a hybrid model is presented in this study to overcome the above problem. The genetic algorithm has been trained and tested only with the same letters, thus the cost of training for the genetic algorithm has significantly decreased. Comparison of the results obtained from the experiments carried out in this study with the results of a similar study shows that the combined KNN-genetic algorithm model significantly improved the detection rate of the letters for all tested cases from 94% to 97.03% . Keywords: Coding, plate recognition, genetics, Iran automobile, Genetic Algorithm DOI : 10.7176/CEIS/10-6-04 Publication date :July 31 st 2019
Highlights
With respect to the huge amounts of automobiles and other vehicles, it is clear that the labor alone and without the use of a computer system can not control and manage such a traffic even in small dimensions such as a public parking lot
Increasing the precision of the letter recognition process and reducing the amount of training are the main advantages of the new hybrid model
The K-NN has been implemented as the first classification method, because it is simple, and is resistant to the intelligent collection, and for large databases, it is effective at zero educational cost
Summary
With respect to the huge amounts of automobiles and other vehicles, it is clear that the labor alone and without the use of a computer system can not control and manage such a traffic even in small dimensions such as a public parking lot. The automatic identification of the license plate replaces the manual operation of the license plate numbering of the vehicles. There are hundreds and maybe thousands of traffic violations every day and special cameras record the offending vehicle. If all these images are to be checked by humans and the plate numbers are entered manually, much time and labor will be wasted. The automatic identification of the license plate and the identification of the number plate with high precision are important. We try to carry out the operation on colorful images because it makes it possible for international plates to identify the type of flag inserted on the plate and its nationality. The first study, which proposed the idea of combining excellent resolution techniques and automotive detection systems, was a lowresistivity high-resolution method in which qualitive images are modeled as a random Markov model in a generalized previous approach which operates on the basis of a double-sided filtering for maximum bias probability
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