Vehicle Plate Number Detection and Recognition Using Improved Algorithm

The growing Tanzanian population currently estimated to be 48 Million people and their use of vehicles as means of transport has kept increasing making enforcing traffic rules and regulations among road users a major challenge. This calls for a need to have an automated system that monitors the motorists with a pre-defined sense of intelligence. A Vehicle Detection and Recognition Algorithm which can provide automated access to relevant information to a number plate from information systems containing and managing databases on vehicle and their movements is required. This paper presents work on developed algorithm that localizes plate area, extract and segment character, and finally recognizes and interprets registration number from vehicle image. MATLAB R2012b Simulation software with Image Processing toolbox is employed. HSV color space image, morphological and statistical analysis operations were integrated and employed to a vehicle image to compute plate number area. In segmentation the properties like aspect ratio, extent, and area ratio were important measurement parameters. Finally, the template matching database and statistical character extracted from car image was correlated to recognize alphanumeric character to deduce car registration number.   Keywords— Character extraction, Detection algorithm, Recognition algorithm, Morphological matching, Template matchin

Vehicle Number Plates Detection and Recognition using improved Algorithms: A Review with Tanzanian Case study

Research Article published by International Journal Of Engineering And Computer Science Volume 3 Issue 5, May 2014Invented in 1976, Number Plates Recognition (NPR) has since found wide commercial applications, making its research prospects challenging and scientifically interesting. A complete NPR system functions by vz steps, license plate; localization, sizing and orientation, normalization, character recognitions and geometric analysis. This paper is a review of NPR preliminary stages; it explains number plate localization, sizing and orientations as well as normalizations sections of the Number Plates Detection and Recognition-Tanzania Case study. MATLAB R2012b is employed in these processes. The input incorporated includes front and rear photographic images of vehicles, for proximity and simulation purposes the ample angle of image is 90 degree +-15. The captured image is converted to gray scale, binarized and edge detection algorithms are used to enhance edges. The output of this stage provides the input feature extraction, segmentation and recognitions