Automated License Plate Detection using a Support Vector Machine

This paper proposes a new method of detecting license plates in images of vehicles where the license plate is shown, and reports the detection results when this method was applied to detection of license plates on vehicles in Japan. This license plate detection process detects only the edge vertical components, and the candidate license plates are narrowed down using the contours obtained by dilation and erosion processing and region fill processing. A SVM (Support Vector Machine) based on negative and positive examples is used to determine whether or not a candidate area is a license plate, and finally the position of the license plate is identified. This study examined how the license plate detection results in license plate and non-license plate images were affected by differences in aspect ratios, differences in brightness between the vehicle body and license plate, and the number of positive and negative examples used for learning. The effectiveness of this method was confirmed to yield a license plate detection rate of approximately 90%.