License Plate Recognition From Still Images and Video Sequences: A Survey

• Published in: IEEE transactions on intelligent transportation systems Vol. 9; no. 3; pp. 377 - 391
• Main Authors: Anagnostopoulos, Christos-Nikolaos E., Anagnostopoulos, Ioannis E., Psoroulas, Ioannis D., Loumos, Vassili, Kayafas, Eleftherios
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.09.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Character recognition; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Ground, air and sea transportation, marine construction; Illumination; Image databases; Image processing; Image recognition; Image segmentation; Information systems. Data bases; Intelligent transportation systems; Intelligent vehicles; license plate identification; License plate recognition; license plate recognition (LPR); license plate segmentation; Licenses; Lighting; Memory organisation. Data processing; Nonuniform; optical character recognition (OCR); Optical character recognition software; Pattern recognition. Digital image processing. Computational geometry; Recognition; Robotics; Software; Video sequences; license plate recognition (LPR); optical character recognition (OCR); Image processing; license plate identification; license plate segmentation; Registration (vehicle); Segmentation; Image databank; Outdoor installation; Video signal; Algorithmics; Processing time; Pattern recognition; Character recognition; Stationary condition; Plate; Luminance; Optical character recognition; Image sequence; Fixed image; Illumination
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2008.922938

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.