A unified architecture for the detection and classification of license plates

A method is presented for the detection and classification of New Zealand license plates in real time. The classifier and detector both use a convolutional network which can efficiently be applied to images and is trained using gradient-based learning. The detector has an error rate of less than one percent for individual characters and can find multiple plates in a single image. The classifier has an error rate of less than two percent. The complete system runs at more than 15 frames per second.