A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition
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License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management. A robust and efficient method for enhancing accuracy of license plate characters recognition based on K Nearest Neighbours (K-NN) classifier is presented in this paper. The system first prepares a contour form of the extracted character, then the angle and distance feature information about the character is extracted and finally K-NN classifier is used to character recognition. Angle and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbours. We evaluated our method on the available dataset that contain 1200 sample. Using 70 for training, we tested our method on whole samples and obtained 99 recognition rate.Further, we achieved average 99.41 three/strategy validation technique on 1200 dataset.
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