Iranian Vehicle License Plate Detection based on Cascade Classifier

A license plate recognition system contains three main steps: plate detection, character segmentation and character recognition. The first and foremost step of this system is the plate detection stage where the plate is located from the input image. In this paper an effective plate detection approach is developed based on a cascade classifier. A two-phase training approach is proposed to enhance the classifier by getting feedback from the negative data;  and then, a new testing approach is suggested to reduce the false-positive detection and to enhance the detection precision. The proposed system is able to be applied on gray as well as color images, such that it can detect multiple plates at front or rear of the cars in different illuminations. Moreover, the proposed method is invariant to the size, position or where the plates are located in the image. The experimental results on real-world images in different conditions show that the proposed approach can improve the precision and recall rate while reducing the false-positive rate.