License Plate Recognition System

Topics: Image processing, Automatic number plate recognition, Mathematical morphology Pages: 9 (2730 words) Published: May 18, 2011
Mathematical Morphology Methodology for Extraction of Vehicle Number Plates Humayun K. Sulehria, Ye Zhang, Danish Irfan
Abstract— This paper presents a methodology for extraction of the vehicle number plates from the vehicle images using hybrid mathematical morphology techniques. The main idea is to use different morphological operations in such a way so that the number plate of the vehicle can be identified precisely. The methodology makes the task of extraction of the number plate independent of color, size and location of number plate. The proposed approach involves five different processes, these are, image enhancement, morphing transformation, morphological gradient, combination of resultant images and extracting the number plate from the objects that are left in the image. This algorithm can quickly and correctly detect the number plate area from the vehicle image. Some vehicle number plate norms are also presented in this paper. Keywords— Mathematical morphology, morphological gradient, vehicle number plate, morphing transformations, image enhancement. I. INTRODUCTION N the current information technology era, the use of automations and intelligent systems is becoming more and more widespread. The Intelligent Transport System (ITS) technology has gotten so much attention that many systems are being developed and applied all over the world. Vehicle number plate recognition (VNPR) has turned out to be an important research issue. VNPR has many applications in traffic monitoring system, including controlling the traffic volume, ticketing vehicle without the human control, vehicle tracking, policing, security, and so on. The most vital and the most difficult part of any VNPR system [11] is the detection and extraction of the vehicle Number plate, which directly affects the systems overall accuracy. The presence of noise, blurring in the image, uneven illumination, dim light and foggy conditions make the task even more difficult. In this paper we propose a detailed and novel method for accurately detecting the location of vehicle number plates. The proposed system can work very accurately in almost any environment, time of day, and conditions. There are some international, national or local standards for vehicles. One sample is presented in the Appendix to this text. In China, the basic norms [12] for the number plate are presented. Some regional co-operations such as European Union (EU), have plates [13] that define the country, the place of registration, etc. In this text, Chinese, Pakistani, and Kuwaiti plates are represented. Manuscript submitted Janaury 10, 2007; Revised received June 1, 2007. H. K. Sulehria is with the Harbin Institute of Technology, Harbin-150001, Heilongjiang PR China (phone: +86-451-86401103; e-mail: hksulehria2001@ Y. Zhang, is with Harbin Institute of Technology, Harbin-150001, Heilongjiang PR China. (e-mail: II. RELATED WORK

The problem of automatic VNP recognition is being studied since the 90’s [5], [8], [10]. The early approaches were based on characteristics of boundary lines. The input image being first processed to enrich and enhance boundary line-information by using such algorithms as the gradient filter, and resulting in an image formed of edges. The image thus processed was converted to its binary counterpart and then processed by certain algorithms, such as Hough transform, to detect lines. Eventually, couples of 2-parallel lines were considered as a plate-designate [6], [11]. Another approach was based on the morphology of objects in an image [1], [7]. This approach focuses on some salient properties of vehicle plate images such as their brightness, contrast, symmetry, angles, etc. Due to these features, this method could be used to detect the similar properties in a certain image and locate the position of number plate regions. The third approach was based on statistical properties of text [3], [4]. In this approach, text regions were discovered...
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