Dense-cluster based voting approach for license plate identification

License plate recognition is a challenging due to different colors of foreground and background especially in Malaysia, where private vehicle (e.g., cars) displays dark background and public vehicle (e.g., taxis/cabs) displays white background. This paper presents a new method called Dense Cluster based Voting (DCV) for identifying an input license plate image as normal or taxi such that suitable recognition algorithms can be used to achieve better recognition rate. The proposed method uses Canny edge image to separate edges as foreground and non-edges as background. Then the proposed method exploits the intensity values corresponding to foreground and background pixels from the input gray image. Next, k-means clustering is proposed to classify intensity values into a Max cluster, which contains high values and a Min cluster, which contains low values for both intensity of foreground and background pixels. This process gives four clusters for the input image. The number of pixels in clusters (dense cluster) and the standard deviation are computed for deriving new hypotheses. Finally, we propose voting for the responses of hypotheses for identification. Classification results with existing methods show that the proposed method outperforms existing methods since the it works based on the distribution of foreground and background pixels rather than character shapes. Furthermore, the recognition results from classification show that recognition rate improves significantly compared to prior classification.