An Improved Method for Tanzania Number Plate Location and Segmentation Based on Mathematical Morphology and Regional Features of an Image

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dc.creatorBulugu, Isack
dc.creatorZhijun, Pei
dc.date2016-09-16T12:18:02Z
dc.date2016-09-16T12:18:02Z
dc.date2013-12
dc.descriptionIn the Automatic Number Plate Recognition System (ANPR), Plate Number Location and Character segmentation are very important parts of an ANPR system before Recognition part. In this paper, plate number localization and character segmentation using mathematical morphological approach and regional features of images are discussed from the proposed ANPR system of vehicles in Tanzania. The proposed algorithm consists of three main modules: Pre-processing (cutting and resizing, convert RGB image to grayscale image, image binarization use Otsu method), Finding Region of Interest(morphology opening to remove noises & dilation operation, measure properties of image regions to find candidates), License plate exactly location (finding the LP angle & rotating LP, cut exactly LP region). The Character segmentation also consist three parts: Eliminate incrimination of the binary using boundary features, removes impurities using regional features and morphological process, and divide character into sub-images. The results show an average of 98% successful plate number localization and segmentation for proposed ANPR system in a total of 200 images captured from a complex outdoor environment in Tanzania. Implementation was done using MATLAB Version 7.11.0 (R2010b).
dc.identifierBulugu, I. and Zhijun, P., An Improved Method for Tanzania Number Plate Location and Segmentation Based on Mathematical Morphology and Regional Features of an Image.
dc.identifierhttp://hdl.handle.net/20.500.11810/3718
dc.languageen
dc.publisherInternational Journal of Science and Research
dc.subjectAutomatic number plate recognition (ANPR)
dc.subjectPlate number location
dc.subjectCharacter segmentation
dc.subjectMATLAB
dc.titleAn Improved Method for Tanzania Number Plate Location and Segmentation Based on Mathematical Morphology and Regional Features of an Image
dc.typeJournal Article, Peer Reviewed

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