Open AccessAn Efficient Methodology for License Plate Localization and Recognition with Low Quality Images
Author(s) -
Sainan Xiao,
Yang Wu,
Buwen Cao,
Honglie Zhou,
Chenjun He
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012084
Subject(s) - artificial intelligence , license , computer science , dirt , computer vision , distortion (music) , pattern recognition (psychology) , support vector machine , enhanced data rates for gsm evolution , engineering , mechanical engineering , amplifier , computer network , bandwidth (computing) , operating system
It is challenging to find an effective license plate detection and recognition method due to the different conditions during the image acquisition phase. This paper aims to develop a new accurate and efficient method based on color difference and SVM recognition model that yields better performance for vehicle images under low quality. The proposed method is tested with 200 images which involve many difficult conditions, such as low resolution, night-lighting, dirt, complicated background, and distortion problems. The experimental results demonstrate very satisfactory performance for license plate detection in terms of speed and accuracy and are better than the existing methods like edge detection or HSV color conversion method. The overall probability of localization is close to 100%, with a false recognition rate of 2%.