This paper presents a morphology-based method for extracting license plates from cluttered images. The proposed system consists of three major components. First, a morphology-based method is proposed for extracting important contrast features as guides to search for desired license plates. The contrast feature is robust to lighting changes and invariant to different transformations like image scaling, translation, and skewing. Then, a recovery algorithm is applied to reconstruct a license plate if it is fragmented into pieces. The last stage of this method is to do license plate verification. The criterion for verification is based on the number of characters appearing in the plate that can be extracted from a clustering algorithm. The morphology-based method can significantly reduce the number of possible characters extracted and thus speeds up subsequent plate recognition. Since the feature extracted is robust to different image changes, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 98%. Due to the simplicity of the proposed method, all the license plates can be extracted very fast (in less than 0.5 s). The experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for license plate detection.