Vehicle Licence Plate Recognition by Fuzzy Artmap Neural Network

Vehicle license plate recognition is one of the techniques that can be used for the identification of vehicles. It is useful to be applied to many applications such as entrance admission, security, parking control, airport or harbour cargo control, road traffic control, speed control and so on. In this paper, we present an approach of recognising vehicle license plate using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family, which has the capability to learn incrementally unlike the conventional BP algorithm. The image of the vehicles are captured by a monochrome CCD camera and processed digitally through a frame grabber. A program is written using Visual C++ and the Matrox Image Library to process the captured image of the vehicle. The image processing technique includes noise reduction and image enhancement, which then finds the region of interest which is the licence plate of the vehicle. Once the ROI is identified, each character or digit in the license plate is isolated and sequentially recognised by the trained neural network. A prototype of the vehicle license plate recognition system, code-named ‘Apple-1’, is currently under development at the Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia.