A New Framework for Vehicle Number Plate Recognition using Data Mining Techniques

Abstract

Data mining is a field at the intersection of computer science is the process that attempts to discover the patterns in large data sets. One of the key steps in Knowledge Discovery in Databases is to create a suitable target data set for the data mining tasks. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, networks and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. The K-Nearest Neighbour algorithm is amongst the simplest of all machine learning algorithms is proposed in this paper. In this proposed approach, the data mining technique is used for edge detection, extraction of plate region, segmentation of plate characters and recognition of characters. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristic by object recognition. In this paper the Modified Sobel edge detection technique is used to detect the edges of the image. With the help of presented technique in this thesis, can detect the number of any plate just by giving as input the image of the plate and number gets extracted and recognized. Here present simplest of all and with lesser complexity to detect the numbers. The image is stored in the form of a matrix and the output is displayed in the form of detected numbers. Experimental Results are carried out in MATLAB and it has been proven that the data mining technique is more efficient and accurate one compared with other techniques.