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License Plate Detection and Character Recognition System

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IJCA Proceedings on International Conference on Communication, Computing and Information Technology
© 2015 by IJCA Journal
ICCCMIT 2014 - Number 1
Year of Publication: 2015
Authors:
M. Sornam
Santhi Manimozhi

M.sornam and Santhi Manimozhi. Article: License Plate Detection and Character Recognition System. IJCA Proceedings on International Conference on Communication, Computing and Information Technology ICCCMIT 2014(1):28-31, March 2015. Full text available. BibTeX

@article{key:article,
	author = {M.sornam and Santhi Manimozhi},
	title = {Article: License Plate Detection and Character Recognition System},
	journal = {IJCA Proceedings on International Conference on Communication, Computing and Information Technology},
	year = {2015},
	volume = {ICCCMIT 2014},
	number = {1},
	pages = {28-31},
	month = {March},
	note = {Full text available}
}

Abstract

Vehicle License Plate Recognition is an image-processing technology and an important field of research that identifies vehicles by their number plates in which the number plate information is extracted from vehicle's image. In this paper, a new algorithm for vehicle license plate identification is proposed, sliding concentric windows (SCW) on the basis of a novel adaptive image segmentation technique. Also Localization algorithm is used in detecting the candidate region. Tilt correction by the Hough transform is used and implemented for estimating rotation angle of the License Plate region. Character Segmentation has been done by extraction bounding of connected component. Optical Character Recognition (OCR) is used to recognize an optically processed printed character number plate which is based on template matching. This algorithm is tested on different ambient illuminated vehicle images. OCR is the last stage in vehicle number plate recognition. In recognition stage, the extracted characters in the number plate are normalized, and the characters are then recognized using the template matching algorithm. This algorithm was tested with 10 vehicle images. The License Plate was successfully located and segmented. The Recognition rate of character using template matching method is 98% accuracy.

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