An automatic technique for Malaysian number plate recognition / Mojeed Salmon Olatunde

License plate recognition is useful for several real time applications, such as traffic monitoring, security issues, tracing transport rules violated vehicles, toll fee payment and intelligent vehicle movement without pilot etc. In order to find solution to license plate recognition, there are many methods developed in literature. However, the existing methods suffer from their own inherent limitations for addressing challenges posed by Malaysian license plate number. One such challenge is that Malaysian license plate where normal plate represented by dark-background, white-foreground (number) and taxi plate represented by white-background and dark-foreground. In addition, some Malaysian license plate suffer from blur, noise, degradations, low contrast and illumination effect. Hence, achieving best recognition rate for the Malaysian license plate number is hard. To alleviate the problem of Malaysian license plate recognition, the work proposes classification of Normal and Taxi plates such that each type can use different recognition method rather than single method for both the type images. The proposed classification method works based on the fact that the values which represent white colour have values near to 255 and the values which represent dark colour have values near to zero. Besides, it is true that the number of background pixels is larger than the number of foreground pixels. Based on these two observation, the proposed classification explores canny edge images of the input image and clustering to differentiate them. For the classified license plate images, The proposed work explores Maximally Stable Extremal Regions (MSER) which perform operation over Canny edge image of the input image unlike existing MSER perform only on grey colour images. This combination outputs character components for license plate images. The components are considered as connected components to separate from the license plate images. The segmented characters are feed to OCR, which is available publicly for recognition. In summary, there are two contributions from the proposed work. One is exploring classification of normal and taxi plate images and another one is use of MSER for character component segmentation. Furthermore, experimental results for classification and recognition on our image dataset show that the proposed method works is better than existing methods