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The Haar-like cascaded classifier has been used in license plate detection and yields a high detection rate, but it often has high false positives. We introduced a classifier which was trained through histogram of oriented gradients (HOG) features to judge the likelihood of candidate plates detected by Haar classifier, and selected the candidate with highest likelihood as the final plate, in order to reduce the false positives. This method was tested on 3000 images to obtain a recall rate of 95.2%, and accuracy of 94.0% as opposed to 66.4% without using HOG features. It was shown that the proposed method is able to eliminate most of the false candidate plates, such as barriers and incomplete plates.