License Plate Recognition From Still Images and Video Sequences: A Survey Christos-Nikolaos E. Anagnostopoulos, Member, IEEE, Ioannis E. Anagnostopoulos, Member, IEEE, Ioannis D. Psoroulas, Vassili Loumos, Member, IEEE, and Eleftherios Kayafas, Member, IEEE
Abstract—License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as ﬁxed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to deﬁne a common reference point for LPR algorithmic assessment. Index Terms—Image processing, license plate identiﬁcation, license plate recognition (LPR), license plate segmentation, optical character recognition (OCR).
In addition, LPR algorithms should operate fast enough to fulﬁll the needs of ITS. In technical terminology, a “real-time” operation for LPR stands for a fast-enough operation to not miss a single object of interest that moves through the scene. Nevertheless, with the exponential growth of the processing power, the latest developments operate within less than 50 ms , ,  for plate detection and recognition (processing more than 20 frames/s for videos). B. Scope of This Survey Papers that follow the three-step framework are surveyed and classiﬁed according to their major methodology. When available, issues such as performance, execution time, and platform for each method are reported. It should be emphasized that there is a lack of uniformity in the way that methods are evaluated, and therefore, it is inappropriate to explicitly declare which methods actually demonstrate the highest performance. Indeed, one of the scopes of this paper is to highlight the lack of common test sets to achieve a common reference point for algorithmic assessment. As the ﬁrst step toward this goal, a large image and video data set of Greek LPs has been collected and grouped according to several criteria such as type and color of plates, illumination conditions, various angles of vision, and indoor or outdoor images at http://www.medialab.ntua.gr/research/LPRdatabase.html. Aiming to present a comprehensive and critical survey of up-to-date LPR methods, this paper is organized as follows: In Section II, we provide a detailed review of techniques to detect LPs in a single image or video sequence. Character segmentation methods and criteria are discussed in Section III, whereas Section IV demonstrates the character classiﬁcation techniques. Finally, this paper concludes with a discussion of current trends and anticipated research in LPR. II. LP D ETECTION
I. I NTRODUCTION A. License Plate Recognition (LPR)
NTELLIGENT transportation systems (ITSs) are made up of 16 types of technology-based systems divided into intelligent infrastructure systems and intelligent vehicle systems . Computer vision and character recognition algorithms for LPR are used as core modules for intelligent infrastructure systems like electronic payment systems (toll payment and parking fee payment) and freeway and arterial management systems for trafﬁc surveillance. LPR algorithms are generally composed of the following three processing steps: 1) location of the license plate (LP) region; 2)...