The research and realization of vehicle license plate character segmentation and recognition technology

Wang, Jianxia; Zhou, Wanzhen; Xue, Jing-Fu; Liu, Xinxin

doi:10.1109/icwapr.2010.5576426

Cited by 17 publications

(8 citation statements)

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“…Zheng et al 5) proposed system is robust under poor illumination conditions and for moving vehicles using modified open source Optical Character Recognition. Wang et al 6) recognize Chinese characters using modified template matching techniques to produce speed and accuracy from previous template matching techniques.…”

Section: A Ocrmentioning

confidence: 99%

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Character Recognition for Indonesian License Plate by Using Image Enhancement and Convolutional Neural Network

Bismantoko

Rosyidi

Chasanah

et al. 2023

Majalah Ilmiah Pengkajian Industri

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Many Intelligent Transport System technology have been applied in real world problems such as traffic monitoring, parking management, toll collection, law enforcement. ALPR system is one of the ITS technologies that is widely applied, however this ALPR system can not produce faultless recognition yet, especially for Indonesia license plate. In this research, image enhancement and Convolution Neural Network are proposed to the character recognition. The dataset used in this research are Indonesia license plate. The first step is train dataset to recognize character and evaluate the model with recall, precision, and f-1 score from test dataset. The model achieves accuracy and loss just over 0.99 and just below 0.01 on validation dataset respectively.Key Words : ALPR; ITS; Recall; Precision; F-1 Score; Accuracy; Loss.

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Section: A Ocrmentioning

confidence: 99%

“…The character recognition algorithm divided into two categories, employ character segmentation and misemploy character segmentation. In addition there are several other algorithms used to recognize characters, including SVM (Support Vector Machine) 2-4) , OCR(Optical Character Recognition) 5,6) and Neural Network [7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Character Recognition for Indonesian License Plate by Using Image Enhancement and Convolutional Neural Network

Bismantoko

Rosyidi

Chasanah

et al. 2023

Majalah Ilmiah Pengkajian Industri

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“…This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. A two-stage approach consisting of Character coarse segmentation method and Character precision segmentation process is adopted in [16]. It can increase the accuracy of the segmentation and has good segmentation speed.…”

Section: Related Workmentioning

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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“…of B=1-no. of hole=1-2=-1 This process continues for all other characters and different Euler groups are classified [10][11][12].…”

Section: Input Layermentioning

confidence: 99%

“…In this paper, license plate characters arrangement is as X1X2 • X3X4X5X6X7, where X1 is Chinese characters, it is shortened forms of the Chinese provinces and cities, X2 is the English letter, it is the licensing authority department code,X3X4X5X6X7 is the combination of English letters and numbers. In order to enhance recognition speed and recognition rate, an improved template matching method is presented [11]. A large and growing literature has been investigated and published on the Artificial intelligence and specially the Automatic vehicle number plate detection and vehicle number extraction.…”

Section: Introductionmentioning

confidence: 99%

Avoidance of confusion between Similar looking Characters in Neuro-fuzzy based License Plate Recognition

Choubey¹,

Sinha²,

Charan³

et al. 2011

IJMLC

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Abstract-This paper presents a new methodology for the image segmentation and character recognition from standard Indian License number plates. Firstly it gets input of the segmented characters that is partitioned by our pixel Clustering partitioning method, in which we eliminate similar part from the character and match it by judging template and return identified character. This partitioning may be applied horizontally or vertically. Decision that the characters are partitioned horizontally or vertically depends on their subgroup. Before sub grouping we have to group the characters on the basis of the number of holes in it and then we subgroup on the basis of some similar features like | , / , \ , _ , ( , -, etc. If we have alphabet T and I where similar portion is I then both will go to same subgroup and we partition it horizontally. This method eliminates the problem of confusion between similar looking elements like C, G and T, I, 1, J etc by exploiting the small but important differences among them.

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The research and realization of vehicle license plate character segmentation and recognition technology

Cited by 17 publications

References 0 publications

Character Recognition for Indonesian License Plate by Using Image Enhancement and Convolutional Neural Network

Character Recognition for Indonesian License Plate by Using Image Enhancement and Convolutional Neural Network

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

Avoidance of confusion between Similar looking Characters in Neuro-fuzzy based License Plate Recognition

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