An Efficient Method of Number Plate Extraction from Indian Vehicles Image

Kaur, Surinder; Sandeep, K.

doi:10.5120/15339-3674

Cited by 11 publications

(15 citation statements)

References 7 publications

(2 reference statements)

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“…Initially image is captured by high quality camera then preprocessing of captured image is performed to reduce the unwanted signals present in the image. In next step the image is converted from RGB to gray scale image based on the threshold value [11].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…One of the best filter, which is widely used in the ANR system is Median filter [5][8] [9]. Sarbjit Kaur and Sukhvir Kaur [11] suggested that, Noise also can be removed by using Iterative bilateral filter [11] in ANR Domain. In [19] they have proposed to blur the image in order to remove the noise.…”

Section: Noise Removalmentioning

confidence: 99%

“…Author Hanit Karwal, Akshay Girdhar have proposed different steps; first convert captured image to gray scale, apply median filter to remove the noise, apply otsu method [8][10] [11]. Nick, Niblack, Souvola [19] only used otsu algorithms for image binarizarion.…”

Section: Noise Removalmentioning

confidence: 99%

“…Nick, Niblack, Souvola [19] only used otsu algorithms for image binarizarion. In addition to image preprocessing author sarbjit kaur [11] has implemented new steps in image preprocessing; After converting from RGB to gray the Iterative bilateral filter is applied to remove the noise then contrast enhancement using adaptive histogram equalization [21] followed by opening and image subtraction (contrast enhanced gray image-opened Image) operation.…”

Section: Noise Removalmentioning

confidence: 99%

“…In [8] the ROI has been extracted by using connected component analysis authors have used the 4 connected components, then labeling the connected component for character segmentation. In [9] number plate is detected using edge detection by sobel operator [11][19] [21] followed by the Hough transform. And then applied the bounding rectangle to extract the number plate.…”

Section: Extract Region Of Interest (Roi)mentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Image Preprocessingmentioning

confidence: 99%

Section: Noise Removalmentioning

confidence: 99%

Section: Noise Removalmentioning

confidence: 99%

Section: Noise Removalmentioning

confidence: 99%

Section: Extract Region Of Interest (Roi)mentioning

confidence: 99%

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Survey on Automatic Number Plate Recognition (ANR)

Sonavane¹,

Soni²,

Majhi³

2015

IJCA

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Helmet detection and license plate recognition

Peter,

Lakshmi,

Prathyusha

et al. 2023

Proceedings of the 1st International Conference on Frontier of Digital Technology Towards a Sustainable Society

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License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions

Khin

Phothisonothai

Choomchuay

2017

2017 International Conference on Advanced Computing and Applications (ACOMP)

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Due to the difficulty in defecting the images, in this paper, we proposed the method that can detect region of interest of the license plate for Myanmar vehicles captured from the dissimilar environmental conditions, e.g., different type of license plates, angle of image capturing, and real environmental conditions. In this paper, the horizontal and vertical dilation, skew angle detection and automatic bounding box were proposed to detect the license number from input images. In our experiment, the obtained results showed that an average detection accuracy of 99% which was substantially applied for the license plate detection from the dissimilar environmental conditions.

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An Efficient Method of Number Plate Extraction from Indian Vehicles Image

Cited by 11 publications

References 7 publications

Survey on Automatic Number Plate Recognition (ANR)

Survey on Automatic Number Plate Recognition (ANR)

Helmet detection and license plate recognition

License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions

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