A Hybrid KNN-SVM Model for Iranian License Plate Recognition

This study presents a new method for Iranian License plate recognition systems that will increase the accuracy and decrease the costs of the recognition phase of these systems. In this regard, ahybrid of the k-Nearest Neighbors algorithmand the Multi-Class Support Vector Machines (KNN-SVM) model was...

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Bibliographic Details
Published in:Procedia computer science 2016, Vol.102, p.588-594
Main Authors: Tabrizi, Sahar S., Cavus, Nadire
Format: Article
Language:English
Subjects:
Feature Extraction
Image Processing
Iranian License Plate Recognition
KNN-SVM Hybrid Model
Machine Learning
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Summary:This study presents a new method for Iranian License plate recognition systems that will increase the accuracy and decrease the costs of the recognition phase of these systems. In this regard, ahybrid of the k-Nearest Neighbors algorithmand the Multi-Class Support Vector Machines (KNN-SVM) model was developedin the study. K-NN was used as the first classification model as it is simple, robust against noisy data set and effective fora large data set. The confusion among the license plate similar characters problem was overcome by using the multiple SVMs classification model. The SVMs model has improved the performance of the K-NN in the recognition of similar characters. The current study experimental results revealed that there is a significant improvement in the character recognition phase rate compared with a similar study.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.09.447