AUTOMATIC DETECTION AND RECOGNITION OF VEHICLE PLATE NUMBERS USING SVM

Abstract

The Automatic license plate reorganization (ALPR) is one of the solutions of such kind of problem. There is a
number of methodologies but it is challenging task as some of the factors like high speed of vehicles, languages of number
plate & mostly non-uniform letter on number plate effects a lot in recognition. The license plate recognition (LPR) system
have many application like payment of parking fees; toll fee on highway; traffic monitoring system; border security system;
signal system etc. In this paper, the different method of license plate recognition is discussed. The systems first detects the
vehicle and capture the image then the number plate of vehicle is extracted from the image using image Segmentation optical
character recognition technique is used for the character recognition. Then the resulting data is compared with the database
record so we come up with the License plate number such as is observed that developed system successfully detects &
recognizes the vehicle number plate on real image even when the pixel is of low resolution and details. The Automatic
Number Plate Recognition (ANPR) was invented in 1976 at the Police Scientific Development Branch in the UK. However, it
gained much popularity during the last decade along with the improvement of digital camera and the increase in processing
speed. ANPR is an image processing technology which enables to extract vehicle license plate number form digital images. It
consists of a camera that has the capability to capture an image, finds the location of the number plate in the image and then
extracts the characters using character recognition tool that translate the pixels into alphanumerically readable character or
string. ANPR can be used in many areas from speed enforcement and tool collection to management of parking lots, etc. At
present, in ANPR there are several techniques used for the recognition plate’s number such as pattern matching [8], neural
network character recognition [1], and image processing technology [2].which are computationally expensive or use
artificial neural network which involves complex mathematics