Morphology-based license plate detection from complex scenes

This paper presents a morphology-based method for detecting license plates from cluttered images. The proposed system consists of three major components. At the first, a morphology-based method is proposed to extract important contrast features as guides to search the desired license plates. The contrast feature is robust to lighting changes and invariant to several transformations like scaling, translation, and skewing. Then, a recovery algorithm is applied for reconstructing a license plate if the plate is fragmented into several parts. The last step is to do license plate verification. The morphology based method can significantly reduce the number of candidates extracted from the cluttered images and thus speeds up the subsequent plate recognition. Under the experimental database, 128 examples got from 130 images were successfully detected. The average accuracy of license plate detection is 98%. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness of license plate detection.