PERFORMANCE AND ANALYSIS OF AUTOMATIC LICENSE PLATE LOCALIZATION AND RECOGNITION FROM VIDEO SEQUENCES

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Engineering , Health Care Sciences & Services

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eISSN: 1178-5608

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VOLUME 10 , ISSUE 4 (December 2017) > List of articles

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Special Issue

PERFORMANCE AND ANALYSIS OF AUTOMATIC LICENSE PLATE LOCALIZATION AND RECOGNITION FROM VIDEO SEQUENCES

M.Anto Bennet * / B. Thamilvalluvan / Priyanka Paree Alphonse / D.R. Thendralarasi / K. Sujithra

Keywords : Plate Recognition (LPR) , Automatic license plate recognition (ALPR) , Optical Character Recognition(OCR).

Citation Information : International Journal on Smart Sensing and Intelligent Systems. VOLUME 10 , ISSUE 4 , Pages 330-343 , ISSN (Online) 1178-5608, DOI: 10.21307/ijssis-2017-255, December 2017

License : (CC BY 4.0)

Received Date : 27-May-2017 / Accepted: 15-June-2017 / Published Online: 01-September-2017

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ABSTRACT

The works presents license plate recognition system using connected component analysis and template matching model for accurate identification. Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system model uses already captured images for this recognition process. First the recognition system starts with character identification based on number plate extraction, Splitting characters and template matching. ALPR as a real life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring. The system uses different templates for identifying the characters from input image. After character recognition, an identified group of characters will be compared with database number plates for authentication. The proposed model has low complexity and less time consuming interms of number plate segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant sample.

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