A Hybrid Approach to License Plate Segmentation under Complex Conditions

A hybrid license plate segmentation approach based on neural network is proposed, which is designed to work under complex acquisition conditions including unrestricted scene and lighting and a wide range of camera-car distances. The approach consists of four stages: preprocessing, candidate regions detecting, real vehicle license plate extracting, and character segmenting. Experiments have proved the robustness and accuracy of the approach. In the experiments databases, which were taken from real scenes, 380 from 400 images were successfully segmented. The average accuracy of segmenting vehicle license plate is 95%.