Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing

Automatic detection, extraction and recognition of vehicle number plate region in traffic control systems is one of the prominent application in Computer vision. The drastic increase in number of vehicles in the current generation greatly increases the complexity in tracking the vehicles through the human visual system, manual procedure of controlling traffic and enforcement of various laws and rules is not sufficient for smooth control of traffic. This urges the need for development of technology that can automate this process. This paper mainly focuses on the development of an automatic number plate extraction and recognition algorithm by incorporating constructs like edge detection, horizontal and vertical edge processing using fixed threshold technique. The extracted number plate region is again processed using template matching algorithm for the recognition of the characters embossed on the number plate with respect to every individual piece of number plate. The algorithm developed has achieved an accuracy of around 100% and works for both front and rear images of the car

SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE

The purpose of this paper is to design and implement an automatic number plate recognition system. The system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and automatically reads it with no prior assumption of background made. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading. The string output is then used to query a relational database to obtain the desired user data. This particular paper utilizes the intersection of a hat filtered image and a texture mask as the means of locating the number plate within the image. The accuracy of location of the number plate with an image set of 100 images is 68%