New method for optimization of license plate recognition system with use of edge detection and connected component

Azad, Reza; Shayegh, Hamid Reza

doi:10.1109/iccke.2013.6682800

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…For evaluation of our method first, the data set that is mentioned in [48][49][50] are considered. This data set consists of 150 colorful images in 640*480 sizes.…”

Section: Experimental and Comparative Resultsmentioning

confidence: 99%

New framework based on complementary methods for efficiency and accuracy of license plate recognition system

Azad

Davami

Jeo

et al. 2014

2014 6th Conference on Information and Knowledge Technology (IKT)

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This paper represents a new and intelligent system for license plate recognition used in many cases, for example: unattended parking lots, security control of restricted areas, traffic law enforcement, congestion pricing, and automatic toll collection. The proposed system is a process based on a combination of the complement methods that increases the accuracy of output data and is generally accountable on the images that have many problems and aren't answerable with a particular approach. The performance of this system is in a form that, firstly, the input image is given to all the complement methods in order for the location of the plate candidate to be determined, then their response is investigated according to the valence function and finally the location of plate is properly determined by the majority of votes and ensures the accuracy of output data. If the complement methods are chosen correctly, this proposed system will be responsive to the images in which the license plate recognition is likely. This method has been tested on two data sets that have different images of the background, considering the distance, and angle of view so that the correct extraction rate of plate location reached at 100% and 99% respectively.

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“…For evaluation of our method first, the data set that is mentioned in [48][49][50] are considered. This data set consists of 150 colorful images in 640*480 sizes.…”

Section: Experimental and Comparative Resultsmentioning

confidence: 99%

New framework based on complementary methods for efficiency and accuracy of license plate recognition system

Azad

Davami

Jeo

et al. 2014

2014 6th Conference on Information and Knowledge Technology (IKT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reza Azad and Hamid Reza Shayegh proposed a fast and real time method which has an appropriate application for tilt plates [5]. Ronak [14].…”

Section: Literature Surveymentioning

confidence: 99%

“…5 The steps 1-4 (image acquisition, RGB to grayscale conversion, Noise removal using iterative bilateral filter, and contrast enhancement using AHE) come under the Image Acquisition and Pre-Processing phase. The steps (5)(6)(7)(8)(9)(10) come under the number plate extraction phase (NPE). The character segmentation consists of connected component analysis and boundary box analysis and finally template loading, character normalization and template matching using correlation is performed in character recognition phase.…”

Section: Proposed Approach For Anprmentioning

confidence: 99%

An Automatic Number Plate Recognition System under Image Processing

Kaur¹

2016

IJISA

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Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases:-Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

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“…Azad and Shayegh [9], use adaptive threshold to obtain binary images and then use edge detection and morphological operations to localize number plates. Hsu et al, [10] proposed edge clustering mechanism for number plate detection, maximally stable extreme region (MSER) detector for character segmentation and LDA based character recognition.…”

Section: Related Workmentioning

confidence: 99%

Projection Profile Based Number Plate Localization and Recognition

Chowdhury¹,

Das²,

Punitha³

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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This paper proposes algorithms to localize vehicle number plates from natural background images, to segment the characters from the localized number plates and to recognize the segmented characters. The reported system is tested on a dataset of 560 sample images captured with different background under various illuminations. The performance accuracy of the proposed system has been calculated at each stage, which is 97.1%, 95.4% and 95.72% for localisation & extraction, character segmentation and character recognition respectively. The proposed method is also capable of localising and recognising multiple number plates in images.

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New method for optimization of license plate recognition system with use of edge detection and connected component

Cited by 12 publications

References 17 publications

New framework based on complementary methods for efficiency and accuracy of license plate recognition system

New framework based on complementary methods for efficiency and accuracy of license plate recognition system

An Automatic Number Plate Recognition System under Image Processing

Projection Profile Based Number Plate Localization and Recognition

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