A New Approach for Vehicle Number Plate Detection

Abstract

Identification of cars and their owners is a tedious and error prone job. The advent of automatic number plate detection can help tackle problems of parking and traffic control. The system is designed using image processing and machine learning. A new system is proposed to improve detection in low light and over exposure conditions. The image of vehicle is captured, which is preprocessed using techniques like grayscale, binarization. The resultant image is passed on for plate localization, for extracting the number plate using CCA (Connected Component Analysis) and ratio analysis. De-noising of number plate is done using various filters. The characters of the number plate are segmented by CCA and ratio analysis as well. Finally, the recognized characters are compared using techniques such as SVC (linear), SVC (poly), SVC (rbf), KNN, Extra Tree Classifier, LR+RF, and SVC+KNN. The proposed techniques help the system to detect well under dim light, over-exposed images and those in which the vehicle is angled.