A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition
This work addresses license plate recognition for traffic monitoring and parking management, but it is incremental as it applies an existing method (K-NN) with new features to a specific dataset.
The paper tackled the problem of improving license plate character recognition accuracy by using angle and distance features with a K-NN classifier, achieving 99% correct recognition on a dataset of 1200 samples and 99.41% average accuracy with cross-validation.
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% samples for training, we tested our method on whole samples and obtained 99% correct recognition rate.Further, we achieved average 99.41% accuracy using three/strategy validation technique on 1200 dataset.