Licence Plate Recognition from Still Images and Videos
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 9, NO. 3, SEPTEMBER 2008
377
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 [3], [152], [156] for plate detection and recognition (processing more than 20 frames/s for videos). B. Scope of This Survey Papers that follow the
377
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 [3], [152], [156] for plate detection and recognition (processing more than 20 frames/s for videos). B. Scope of This Survey Papers that follow the