Adaptive enhancement and binarization techniques for degraded plate images

Abstract

Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4 k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.