A Morphology Based Approach for Car License Plate Extraction

Suryanarayana, Phanish; Mitra, Sunanda; Banerjee, Ayan; Roy, Anil K.

doi:10.1109/indcon.2005.1590116

Cited by 35 publications

(17 citation statements)

References 4 publications

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“…The most common approaches are based on filtering strategies and feature matching methods. There are methods based on projective invariance [28], Hough transform [29], color or texture features [30][31], morphology [32], artificial neural networks [33], SVM classifiers to model character appearance variations [34], and methods based on top-hat transform [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 establishes a relationship between this lower edge location in the image (SVP), the real vehicle's distance (Dist) and the character's thickness (CT). In Table 1, the structuring element SE After the operator Top-Hat is applied, the binarization threshold of the still gray image is established by means of the Otsu method [38] as in [32,35]. Fig.…”

Section: Number Plate Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Arenado

Oria

Torre-Ferrero

et al. 2014

IET Intelligent Transport Systems

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Abstract-This paper presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an Intelligent Transportation System (ITS). The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection we use a combination of two features: the shadow underneath the vehicle and horizontal edges. We propose a new method for shadow thresholding based on the grayscale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardized in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

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Section: Related Workmentioning

confidence: 99%

Section: Number Plate Detectionmentioning

confidence: 99%

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Arenado

Oria

Torre-Ferrero

et al. 2014

IET Intelligent Transport Systems

View full text Add to dashboard Cite

show abstract

“…In the binary image processing methods to extract license plate regions from background images, techniques based on combinations of edge statistics and morphology could achieve good results [3][4][5] . However, such methods are typically based on a hypothesis that the edges of the license plate frames are clear and horizontal.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Novel License Plate Detection Method Based on Heuristic Energy

Sarker¹,

Yoon²,

Lee³

et al. 2013

The Journal of Korean Institute of Communications and Informati

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License Plate Detection (LPD) is a key component in automatic license plate recognition system. Despite the success of License Plate Recognition (LPR) methods in the past decades, the problem is quite a challenge due to the diversity of plate formats and multiform outdoor illumination conditions during image acquisition. This paper aims at automatical detection of car license plates via image processing techniques. In this paper, we proposed a real-time and robust method for license plate detection using Heuristic Energy Map(HEM). In the vehicle image, the region of license plate contains many components or edges. We obtain the edge energy values of an image by using the box filter and search for the license plate region with high energy values. Using this energy value information or Heuristic Energy Map(HEM), we can easily detect the license plate region from vehicle image with a very high possibilities. The proposed method consists two main steps: Region of Interest (ROI) Detection and License Plate Detection. This method has better performance in speed and accuracy than the most of existing methods used for license plate detection. The proposed method can detect a license plate within 130 milliseconds and its detection rate is 99.2% on a 3.10-GHz Intel Core i3-2100(with 4.00 GB of RAM) personal computer.

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“…A desired LPR system has to work in various situations, such as low contrast, blurring due to motion, dirty plates and dynamic lighting changes due to varying weather conditions. Hence car plate detection is the most difficult and crucial task among these steps [1]- [4]. During many years different approaches for car plate detection have been proposed, including edge analysis, neural networks, color and fuzzy wavelet and Gabor analysis, etc [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Top Hat Transform technique on Indian license plate image localization

Arulmozhi¹,

Perumal

Sanooj³

et al. 2012

2012 IEEE International Conference on Computational Intelligence and Computing Research

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Automatic License Plate Recognition (ALPR)is used in Intelligent Transportation systems wherein they are deployed for automatic ticketing of vehicles at car parking area, tracking vehicles during traffic signal violations and related applications. Out of the three major modules of ALPR systems, namely 1. localization of license plate from vehicle image, 2. segmentation of the characters images from the localized license plate and 3.Recognition of segmented characters images as license plate number, localization of potential license plate from the vehicle images is the most challenging task due to the huge variations in size, shape, color, texture and spatial orientations of license plate regions in such images. LP Localization fails often due to the presence of complex background and non-uniform illumination of license plate due to varying lighting conditions. In this paper, we propose a smart, simple and efficient algorithm for Indian license Plate Localization using Top Hat Transformation, which suppresses the background of image and remove the non uniform illumination. The algorithm is tested with live ALPR field images, confirming the robustness of the proposed method against adverse imaging condition.

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A Morphology Based Approach for Car License Plate Extraction

Cited by 35 publications

References 4 publications

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Monovision‐based vehicle detection, distance and relative speed measurement in urban traffic

Novel License Plate Detection Method Based on Heuristic Energy

Application of Top Hat Transform technique on Indian license plate image localization

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