Learning-based approach for license plate recognition

This paper presents a learning-based approach for the construction of license plate recognition system. The system consists of three modules. They are respectively, car detection module, license plate segmentation module and recognition module. Car detection module detects a car in the given image sequence obtained from the camera with simple color-based approach. Segmentation module extracts the license plate in detected car image using neural networks (NNs) as filters for analyzing the color and texture properties of license plate. Recognition module then reads characters in detected license plate with support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from tollgate and parking lot, etc, and have shown the following performances on average: Car detection rate 100%, segmentation rate 97.5 %, and character recognition rate about 97.2%.