An edge-based color-aided method for license plate detection

In this paper, the problem of license plate detection is considered. Low quality images due to severe illumination conditions, vehicle motion, viewpoint and distance changes, complex background, etc. are some of popular problems which have to be considered. In order to alleviate these problems, two different image enhancement methods (using intensity variance and edge density) are proposed. The aim is to increase contrast of plate-like regions to avoid missing plate location especially in poor quality images. Furthermore, a novel match filter is designed to detect candidate regions as plate. This filter models the vertical edge density of plate region regarding its neighborhood. As the filtering procedure is simple, this approach can be used for real-time applications. In the proposed method, we also use colored texture in the plate as a cue for plate detection. This feature is preserved under viewpoint change. In order to characterize the color information in plate, the MNS (multimodal neighborhood signature) method is used. A well-organized database, consisting of car images with different known distances and viewing angels have been prepared to verify the performance of plate detection algorithm. This database can be used to establish a precise evaluation of the proposed method and any other related work. The results of experiments on different type of car images in complex scenes confirm the robustness of proposed method against severe imaging conditions.