License Plate Characters Segmentation Using Projection and Template Matching

Hongyao, Deng; Xiuli, Song

doi:10.1109/itcs.2009.116

Cited by 15 publications

(5 citation statements)

References 4 publications

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“…Traditional image processing and computer vision techniques for segmentation and/or recognition used in ALPR solutions are many and varied, and include the following: template matching [14,15], connected component analysis [16,17], blob analysis [18], mathematical morphology [15], and DSP-based transforms [19]. These methods, however, tend to fail across unideal plate conditions, including dirt and shadows.…”

Section: Related Researchmentioning

confidence: 99%

License plate segmentation and recognition system using deep learning and OpenVINO

Castro-Zunti¹,

Yepez²,

Ko³

2020

IET Intelligent Transport Systems

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Section: Related Researchmentioning

confidence: 99%

License plate segmentation and recognition system using deep learning and OpenVINO

Castro-Zunti¹,

Yepez²,

Ko³

2020

IET Intelligent Transport Systems

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“…Template matching is a high level machine vision technique for detection and recognition of objects in computer vision community. Many techniques like Grayscale based matching, Naïve template matching, image correlation matching pattern correlation matching, edge based matching, image correlation matching etc have developed [20].…”

Section: Template Matchingmentioning

confidence: 99%

A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

Khan¹,

Yadav²,

Agarwal³

2018

IJET

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Licensed Number plate recognition plays vital role in smart cities for maintaining Law & Order and traffic management. NPR based system mainly involves four stages namely 1) Image capture & Pre-Processing 2) Number plate area determination 3) Character Segmentations 4) Recognition of all character. This survey paper extensively analyzed the method of extraction of number plate, its platform, performance and execution time. With the development of Multilayer Perceptron Network accuracy and time in image processing has been achieved up to a great instant. Hence this analysis will help the precise assessment in establishing research and enable developers to assess which strategies are aggressive in present environment.

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“…In recent years many methods for license plate recognition systems have been proposed. In [3], for overcome the problems of quality of image that be shot in the natural environment is poor generally. Use projecting the image to horizontal direction can detects the approximate edge of single license character in the vertical direction.…”

Section: Related Workmentioning

confidence: 99%

“…The level of characters segmentation accuracy has great impact for characters recognition. In the same conditions, more level the accuracy of characters segmentation is, more higher the probability of characters recognition [3]. The segmentation of a line of characters is an important problem emerging in the LPR systems.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a High Speed Technique for Character Segmentation of License Plate Based on Thresholding Algorithm

Rashidi

2012

IJIGSP

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This paper presents, complete step by step description design and implementation of a high speed technique for character segmentation of license plate based on thresholding algorithm. Because of vertical edges in the plate, fast Sobel edge detection has been used for extracting location of license plate, after stage edge detection the image is segmented by thresholding algorithm and the color of characters is changed to white and the color of background is black. Then, boundary's pixels of license plate are scanned and their color is changed to black pixels. Afterward the image is scanned vertically and if the number of black pixels in a column is equal to the width of plate or a little few, then the pixels of that column is changed to white pixel, until create white columns between characters, in continue we change pixels around license plate to white pixels. Finally characters are segmented cleanly. We test proposed character segmentation algorithm for stage recognition of number by code that we design. Results of experimentation on different images demonstrate ability of proposed algorithm. The accuracy of proposed character segmentation is 99% and average time of character segmentation is 15ms with thresholding algorithm code and 0.7ms only segmentation character code that is very small in comparison with other algorithms.

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License Plate Characters Segmentation Using Projection and Template Matching

Cited by 15 publications

References 4 publications

License plate segmentation and recognition system using deep learning and OpenVINO

License plate segmentation and recognition system using deep learning and OpenVINO

A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

Implementation of a High Speed Technique for Character Segmentation of License Plate Based on Thresholding Algorithm

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