Adaptive enhancement and binarization techniques for degraded plate images

作者：Kashef Shima^*; Nezamabadi-pour Hossein; Rashedi Esmat

来源：Multimedia Tools and Applications, 2018, 77(13): 16579-16595.

DOI：10.1007/s11042-017-5229-1

• license plate recognition (LPR)
• contrast enhancement
• background removal
• shadow removal
• binarization

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.

• 出版日期2018-7