Realtime Automatic Vehicle License Plate Recognition

Abstract

License plates provide the unique signature used for vehicle identification worldwide. With pervasiveness of cameras, accurate recognition of plates has becoming an increasingly popular machine-vision problem and has gained a lot of attention due to its wide applications. Contemporary license plate recognition systems work well in controlled environments, with images of good character visibility, but they are not robust and do not perform well in real world scenarios. This work presents a deep neural network approach towards automatic license plate recognition where we formulate it as an object detection problem i.e. each character is treated as an object. The proposed approach is robust and performs under various vision challenges (occlusion, light changes, size variation) owing to both architectural design and the problem formulation of targeting generic printed character recognition. Our method is evaluated on both publicly available, academic and commercial datasets. Furthermore, we also release the largest dataset of license plates for the research community. The proposed ALPR system competitively outperforms the state-of-the-art systems with outstanding recognition results on all license plate datasets.