A flexible method for recognizing four-digit numbers on a license-plate in a video scene

Haneda, Kotaro; Hanaizumi, Hiroshi

doi:10.1109/icit.2012.6209923

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…The method proposed by Bai and Liu in paper [4] is superior to the proposed approach with a remarkable LP detection success rate of 99.6%, which supersedes all other methods. The methods proposed by Huang et al in paper [8], Wen et al in paper [9], Yang et al in paper [5], Haneda and Hanaizumi in paper [10], and Anagnostopoulos et al in paper [6] reported 97.55%, 97.16%, 95.3%, 97%, and 96.5% LP detection rates, respectively, which are less than the proposed method's success rate of 98.74%. Lee et al in paper [3] and Al-Ghaili et al in paper [12] reported 91.25% and 91.65% success rates, but their data set contains only cars.…”

Section: S Number Figure Numbermentioning

confidence: 81%

“…Haneda and Hanaizumi in paper [10] proposed RELIP algorithm, which performs a global search for the probable LP using multiple templates, 3D cross-correlation function, and Principal Component Expansion. This paper uses corner detection to remove deformation of LPs.…”

Section: Existing Similar Researchmentioning

confidence: 99%

“…RELIP reported 97% LP detection success rate. The drawback of paper [10] is that it uses spatial similarity with an LP/LPs template to detect the LP which is not a generic solution in the real world context as the LPs having various deformations such as tilt, rotation, and pan from various viewpoints. Zhou et al in paper [11] proposed a new approach for LP detection based on Principal Visual Word (PVW) discovering and visual word matching.…”

Section: Existing Similar Researchmentioning

confidence: 99%

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Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Soora

Deshpande

2016

Mathematical Problems in Engineering

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Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.

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Section: S Number Figure Numbermentioning

confidence: 81%

Section: Existing Similar Researchmentioning

confidence: 99%

Section: Existing Similar Researchmentioning

confidence: 99%

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Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Soora

Deshpande

2016

Mathematical Problems in Engineering

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“…Mingdong [12] proposed a method focused on Chinese characters which used local binary pattern combined with horizontal and vertical projection algorithm for feature extraction, followed by backpropagation (BP) neural network (NN) to identify the LP characters. Haneda and Hanaizumi [13] proposed a method that performs a global search for the probable LP using multiple templates, a 3D cross‐correlation function, and principal component expansion. The method uses corner detection to remove deformations of the LPs and uses spatial similarity with an LP template to detect the LP.…”

Section: Literature Reviewmentioning

confidence: 99%

Locating multiple license plates using scale, rotation, and colour‐independent clustering and filtering techniques

Bhargav

Deshpande

2019

IET image process

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A license plate (LP) can help identify a motor vehicle. However, no common standard for LP exists across countries, and even within a country, significant LP variations are observed. In addition, environmental factors cause uncontrolled plate and character variations. It is, therefore, a challenge to design a robust and universal license plate recognition (LPR) system which works for multiple countries, for different types of vehicles, and for different styles of LPs. This study presents a novel approach for locating LP based on the use of multiple clustering and filtering techniques applied to the geometrical properties of LP characters. The proposed approach is independent of the size, rotation, and colour of the LP and can be used to locate single or multiple LP of different styles of different vehicles and of different countries. The approach has been validated using the standard Media‐lab and application‐oriented license plate (AOLP) datasets as well as on datasets of vehicles from other countries. The approach achieved an average success ratio of 93.42% for locating LPs from both the Media‐lab and the AOLP dataset and is higher than the results of previously published methods which evaluated their performance over the same datasets.

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“…After extraction they have applied the Smearing algorithm [17] to extract the text area region from the image followed by morphological operations. In [14] the global search algorithm has been used for finding the number plate in an image and extracting only 4 digit from the number plate. In paper [10] they have used the bounding rectangle over license plate.…”

Section: Extract Region Of Interest (Roi)mentioning

confidence: 99%

Survey on Automatic Number Plate Recognition (ANR)

Sonavane¹,

Soni²,

Majhi³

2015

IJCA

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Day by day we have been heard about the news of vehicle getting stolen from parking or from any other place in the city. So, to keep track of that stolen vehicle we should have to install the CCTV camera on every signal in the city. Also we have to install the number plate detection system which can detect the number plate of every vehicle on the traffic signal. For detecting the number plate from the moving vehicle there are many algorithm has been developed but still this area always remain evolving each every year, In number plate detection system image processing plays an important role, the system consist of basic operations preprocessing, image conversion from RGB to Gray, apply edge detection, apply morphological operators on same image then extract plate region from image and last process the plate region using OCR (optical character recognition).Every algorithm in this category always follows this basic steps, each algorithm has some pros and cons, because same algorithm cannot be useful for different environmental condition. The Algorithm's efficiency is totally depends upon the quality of input image. E.g. resolution of camera, intensity of the image, illumination of image, shadow effect etc. In this paper we are focusing on which are different algorithm has been developed so far to improve the efficiency of the number plate detection system. General TermsTemplate Matching, Neural Network.

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A flexible method for recognizing four-digit numbers on a license-plate in a video scene

Cited by 6 publications

References 7 publications

Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Locating multiple license plates using scale, rotation, and colour‐independent clustering and filtering techniques

Survey on Automatic Number Plate Recognition (ANR)

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