Locating multiple license plates using scale, rotation, and colour‐independent clustering and filtering techniques. Issue 12 (17th September 2019)

• Record Type: Journal Article
• Authors: Bhargav, Rohit; Deshpande, Parag
• Is Part Of: IET image processing. Volume 13:Issue 12(2019)
• Page Start: 2335
• Page End: 2345
• Publication Date: 2019-09-17
• Subjects: traffic engineering computing -- edge detection -- object detection -- road vehicles -- image recognition -- feature extraction -- character recognition; locating LPs -- multiple license plates -- colour‐independent clustering -- filtering techniques -- motor vehicle -- common standard -- significant LP variations -- environmental factors -- uncontrolled plate -- character variations -- multiple countries -- multiple clustering -- LP characters -- standard Media‐lab -- application‐oriented license plate datasets; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6237
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A license plate (LP) can help identify a motor vehicle. However, no common standard for LP exists across countries, and even within a country, significant LP variations are observed. In addition, environmental factors cause uncontrolled plate and character variations. It is, therefore, a challenge to design a robust and universal license plate recognition (LPR) system which works for multiple countries, for different types of vehicles, and for different styles of LPs. This study presents a novel approach for locating LP based on the use of multiple clustering and filtering techniques applied to the geometrical properties of LP characters. The proposed approach is independent of the size, rotation, and colour of the LP and can be used to locate single or multiple LP of different styles of different vehicles and of different countries. The approach has been validated using the standard Media‐lab and application‐oriented license plate (AOLP) datasets as well as on datasets of vehicles from other countries. The approach achieved an average success ratio of 93.42% for locating LPs from both the Media‐lab and the AOLP dataset and is higher than the results of previously published methods which evaluated their performance over the same datasets.