Systematic review on vehicular licence plate recognition framework in intelligent transport systems. Issue 5 (25th January 2019)

• Record Type: Journal Article
• Authors: Arafat, Md Yeasir; Khairuddin, Anis Salwa Mohd; Khairuddin, Uswah; Paramesran, Raveendran
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 5(2019)
• Page Start: 745
• Page End: 755
• Publication Date: 2019-01-25
• Subjects: image recognition -- road vehicles -- object recognition -- image segmentation -- object detection -- intelligent transportation systems -- image colour analysis -- image restoration -- image classification; vehicular licence plate recognition -- VLPR techniques -- vehicular speed -- recognition rate -- vehicle image -- intelligent transport systems; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5151
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : In recent years, vehicular licence plate recognition (VLPR) framework has emerged as one of the most significant issues in intelligent transport systems. It has emerged as an important and complicated issue of research in recent times as explorations are carried on this issue with regard to the challenges and diversities of licence plates (LP) including various illumination and hazardous situations. Restricted situations like stationary background, only one vehicle image, fixed illumination, and limited vehicular speed have been focused in most of the approaches. VLPR approaches should be generalised for being capable of identifying LP containing different fonts, colours, languages, complex backgrounds, deformities, hazardous situations, occlusion, speeding vehicles, vertical or horizontal skew, blurriness, and illumination diversions. A comprehensive investigation on the existing VLPR techniques has been carried throughout this study by the aspects of detecting, segmenting, and recognising the plates. Different existing VLPR approaches have been categorised in accordance with the deployed attributes and the classifications have been compared as well on the basis of conveniences, inconveniences, processing time, and recognition rate when available.