SVM Based License Plate Recognition System

In this paper, we review the use of support vector machine concept in license plate recognition. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. In simple words, given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. Here we are using the concept of SVM in LPR systems. Then a number plate recognition algorithm is proposed for character segmentation and recognition. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. Multi-class SVMs are developed to classify the given number plate candidate. The experimental results show that our new method is of higher recognition accuracy and higher processing speed than using traditional SVM based multi-class classifier. This new approach provides a good direction for automatic number plate recognition. Here we can conclude SVM is better than any other supervised learning. KeywordsAutomatic Number Plate Recognition, Support vector machine.