Detection and Classification of License Plate by Neural Network Classifier

A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.