LAUSR

Traffic car images suffer immensely from various degrading factors that make it hard to localize license plates. Each license plate localization (LPL) method has its own advantages and disadvantages to extract plates in the images under different circumstances. To have the benefits of different methods, our proposed solution is to employ a combination of four methods including a method based on cascade classifiers and local binary pattern (LBP) features, an edge-based method, a color-based method, and a contrast-based method. Considering the computational complexity, the methods are ordered on the basis of their chances for success. The order of the methods and the parameters are set experimentally in different conditions: day, night, and twilight. Furthermore, to find the plates rapidly, an algorithm is proposed to refine regions of interest (ROIs) and remove unwanted regions. The algorithm is applied in a real automated transport system for plate identification/recognition and tested with 4000 vehicle images taken from a three-lane dual carriageway with a central barrier in the different illumination situations with six cameras. The results are promising in a large database of moving car images. The car license plates have been correctly extracted in 3938 input images (98.45%). The results show that the proposed system is robust for moving cars in outdoor and under different illumination conditions.