License Plate Recognition Model Research Based on the Multi-Feature Technology

Abstract

Due to the impact of pollution, environment and so on in actual scene, it is difficult for the traditional single feature recognition model to obtain a higher accuracy of the license plate recognition. The paper proposed a new license plate image recognition model. First, the structural features and gray level features of the license plate, such as the contour and stroke order are extracted. Then, the principal component analysis is used to carry out the fusion, dimensionality reduction and redundancy removal processing for the two kinds of features, and a fuzzy fusion model for differentiated features is introduced to ensure minimal loss for the feature in fusion. Finally, the final result of the license plate image recognition is achieved in accordance with high degree of confidence criterion when the slope interference of the license plate is considered fully. Simulation results show that the license plate image recognition model based on multi-feature combination can solve the problem of the single feature recognition model, improve the accuracy of license plate recognition which is up to 99%. Moreover, the model has a faster recognition speed and can be applied to the actual license plate recognition. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4307