Application-Oriented License Plate Recognition

Hsu, Gee-Sern; Chen, Jiun-Chang; Chung, Yu-Zu

doi:10.1109/tvt.2012.2226218

Cited by 281 publications

(194 citation statements)

References 27 publications

Supporting

Mentioning

191

Contrasting

Unclassified

Order By: Relevance

“…In this procedure, subtle fractures can be linked and tiny abrupt changes can be softened. The main aim of this technique is to prevent the destruction of image edges, to retain the image outline and lines as much as possible, increasing the contrast between the ROI and other regions A bi-layer classifier, which is improved with an additional null class, is experimentally proven to be better than previous methods for character recognition [11].…”

Section: Literature Surveymentioning

confidence: 99%

A Survey and Comparison of License Plate Recognition using different Classifiers

Utge¹

2015

IJCA

View full text Add to dashboard Cite

LPR is a well know image processing technology. Now a days vehicles play a very big role in transportation. Also the use of vehicles has been increasing because of population growth and human needs in recent years. Therefore, control of vehicles is becoming a big problem and much more difficult to solve . license plate are available in various colors and style The presence of noise, blurring in the image, uneven illumination, dim light and foggy conditions make the task even more difficult. In the proposed system ANN classifier will be used. Firstly preprocessing techniques will be done on input image, such as grayscale, blurring ,thresholding and frequency calculation then segmentation. Finally use Artificial neural network classifier as a recognition method. In this paper a survey has done compared and analyzed with different recognition method.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Survey and Comparison of License Plate Recognition using different Classifiers

Utge¹

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Generally, any image could be denoted by the three primary colors of RGB [3], which includes much information needing to be managed. So it will waste a lot of time to deal with redundant information, especially the processing of license plate characters recognition.…”

Section: Related Workmentioning

confidence: 99%

An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

Zhong¹,

Chang²,

Chen³

et al. 2014

TOEEJ

View full text Add to dashboard Cite

License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2%. The experiment results prove that the improved character recognition method is accurate and highly consistent.

show abstract

“…The localization accuracy can greatly affect the recognition rate. Due to the presence of dense edge sets, edgebased methods [1][2][3][4][5][6][7] are the most popular ways to localize the license plates. Texture 8,9 or the combinations of colors [10][11][12] are also considered as key features for license plate detection.…”

Section: Introductionmentioning

confidence: 99%

“…Other popular approaches are artificial neural networks 18,37-39 and classifiers. 6,40,41 Convolutional neural network (CNN) is one of the ways to perform deep learning, where raw images can be directly used as inputs. Because of its local receptive fields, shared weights and the spatial subsampling, CNN has the advantage of shift, scale, and noise distortion invariance.…”

Section: Introductionmentioning

confidence: 99%

Vehicle license plate recognition using visual attention model and deep learning

Zang

Chai

Zhang

et al. 2015

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. A vehicle's license plate is the unique feature by which to identify each individual vehicle. As an important research area of an intelligent transportation system, the recognition of vehicle license plates has been investigated for some decades. An approach based on a visual attention model and deep learning is proposed to handle the problem of Chinese car license plate recognition for traffic videos. We first use a modified visual attention model to locate the license plate, and then the license plate is segmented into seven blocks using a projection method. Two classifiers, which combine the advantages of convolutional neural network-based feature learning and support vector machine for multichannel processing, are designed to recognize Chinese characters, numbers, and alphabet letters, respectively. Experimental results demonstrate that the presented method can achieve high recognition accuracy and works robustly even under the conditions of illumination change and noise contamination. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Application-Oriented License Plate Recognition

Cited by 281 publications

References 27 publications

A Survey and Comparison of License Plate Recognition using different Classifiers

A Survey and Comparison of License Plate Recognition using different Classifiers

An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

Vehicle license plate recognition using visual attention model and deep learning

Contact Info

Product

Resources

About