An Automated Vision System for Vehicle License Plate Localization, Segmentation in Real Life Scene

Abstract

In this paper, we propose an image-based approach to detect the presence of a vehicle license plate (VLP) in a traffic image. This method is based on the fractal character difference between the VLP region and the other regions. The technique extracts information from fractal feature blocks and computes the dimension zone that describes the difference between the license plate and the other areas in the scene. A new fractal-dimension computing method aimed at gray-image data has been presented and been applied to image segmentation. Three indexes have been defined to measure the performance of the method. The fractal-dimension parameter’s choice and self-adapting adjustment have been discussed in detail. Further, the proposed system has been evaluated using test and verification datasets and has been proven to be robust against common issues of traffic images, such as varying illumination, camera vibration, insensitivity to vehicle plate’s color and location, and high noise levels. Since there is always a large number of fractal characteristic blocks around the same spatial location in traffic video frames, the efficiency of the proposed algorithm for video will be greatly improved.