Number plate recognition in noisy image

Abstract

Number plate recognition has been used widely for access control, congestion control, vehicle management, security control and vehicle behavior monitoring system. This study discusses the importance of number plate recognition and its corresponding application in different countries. Various methods for recognizing number plates are reviewed. Most of the systems are able to deliver good recognition rate of above 90%. However, there is a lack of literature reporting number plate recognition in images with noisy background. We propose and report a system that is able to tolerate noise level up to 20% with recognition rate of 85%. The system utilized a combination of filters and morphological transformation for segmenting the number plate. It then uses resilient back-propagation neural networks for recognition.