Robust Number Plate Recognition in Image Sequences

Abstract

License plate detection is done in three steps. The localization of the plate, the segmentation of the characters and the classification of the characters are the main steps to classify a license plate. Different algorithms for each of these steps are used depending on the area of usage. Corner detection or edge projection is used to localize the plate. Different algorithms are also available for character segmentation and character classification. A license plate is classified once for each car in images and in video streams, therefore it can happen that the single picture of the car is taken under bad lighting conditions or other bad conditions. In order to improve the recognition rate, it is not necessary to enhance character training or improve the localization and segmentation of the characters. In case of image sequences, temporal information of the existing license plate in consecutive frames can be used for statistical analysis to improve the recognition rate. In this paper an existing approach for a single classification of license plates and a new approach of license plate recognition in image sequences are presented. The motivation of using the information in image sequences and therefore classify one car multiple times is to have a more robust and converging classification where wrong single classifications can be suppressed.