License Plate Character Recognition Using Block-Binary-Pixel-Sum Features

摘要

Since license plate character recognition plays a very important role in vehicle control, such as electronic toll collection (ETC) for highways and management for parking lots, the cost of management can be reduced and the implementing efficiency can be promoted by automatizing license plate character recognition. As the technology of image processing, classifiers, and computational speed on computer advances, we adopt Sobel operators to detect the boundaries of objects in order to extract license plate regions. After extracting license plate regions, we segment corresponding characters and then standardize these characters in order to find out the features of characters, and finally use the classifiers of support vector machine (SVM) and K-nearest neighbor (KNN) to train and then recognize characters. Experimental results show that classifiers and features are closely linked, and KNN is more appropriate for block-binary-pixel-sum features than SVM, and its recognition rate is up to 98.51% on average.