Abstract
License Plate Recognition (LPR) is the most importa nt type of Intelligent Transportation System (ITS). LPR is used in many different types of ITS like ele ctronic payment systems, toll station, parking fees , freeway and arterial management systems for traffic surveillance. Few years ago, Egyptians government changed the car license plate to include letters an d numbers. So the needs for efficient LPR System fo r the new license plate are increased in different ITS fi elds. This study presents an enhanced LPR detection algorithm for the new Egyptian licenses plate. The detection enhancement is done using Stroke Width Transform algorithm to extract letters from candida te areas combined with Fuzzy ARTMAP classifier. Stroke Width Transform (SWT) is a state of art algo rithm developed by Microsoft Research Lab for detecting text in natural scene, it seeks to find t he value of stroke width for each image pixel and demonstrate its use on the task of text detection i n natural images. This study is focusing on detecti ng Arabic letters in the candidate license plate area using SWT image map instead of binary image map where not all Arabic letters have uniformly stroke width and some letters have a dot above and below it. The proposed model shows 26% detection accuracy enhancement than conventional LPR systems (Sobel Edge detection with binary image map using template matching technique).
Highlights
License Plate Recognition (LPR) system is effective in crowded cities where the controlling of vehicles becomes a big problem and LPR is kind of Intelligent Transport Systems (ITS) that difficult to solve
LPR models are composed of three processing steps: plate, LPR is used as core modules for intelligent (1) License plate detection, (2) Character segmentation, infrastructure systems like (electronic payment systems, (3) Character recognition
After detecting the start point and end point of each character, it is cropped into separated image and resized to 70×50, which is the size of letters trained by Fuzzy ARTMAP and extracts its geometrical features (Ghosh, 2010) to be sent to Fuzzy ARTMAP to be classified and its ASCII values returned
Summary
LPR system is effective in crowded cities where the controlling of vehicles becomes a big problem and LPR is kind of Intelligent Transport Systems (ITS) that difficult to solve. Stroke Width Transformation (SWT) (Epshtein et al, 2010) is a novel state of art image operator for detecting text from natural scene; SWT converts the image from an array contains gray values to an array contains likely stroke widths for each pixel. This information suffices for extracting the text by measuring the width variance in each component since text tends to maintain fixed stroke width which puts it apart from other image elements such as foliage.
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