Detection of license plate characters in natural scene with MSER and SIFT unigram classifier

We present a license plate detector using a fusion of Maximally Stable Extremal Regions (MSER) and SIFT-based unigram classifier trained with Core Vector Machine (CVM). First, MSER is used to obtain a set of regions. Highly unlikely regions are removed with a simplistic heuristic-based filter. Finally, remaining regions with sufficient positively classified SIFT keypoint are retained as likely license plate regions. To train the unigram classifier, a set of SIFT keypoints are obtained from a small set of ground truth images where the license plates are labeled. The training of the SIFT-based unigram classifier is found to be optimal when a CVM is used. On our testing data set, we got a recall rate of 0.98 and a precision rate of 0.964641. On the Caltech Cars (Rear) data set, a recall rate of 0.904762 and precision rate of 0.837349 is obtained.