A hybrid technique for License Plate Recognition based on feature selection of wavelet transform and artificial neural network

Sharma, Jitendra; Mishra, Amit; Saxena, Khushboo; Kumar, Shiv

doi:10.1109/icroit.2014.6798352

Cited by 18 publications

(7 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The common used learning methods include support vector machines [33], artificial neural networks [26], PNN [17,18], hidden Markov model (HMM) [32], etc. Some researchers integrate multiple features [33], or combine multiple classifiers [35] to improve the recognition accuracy.…”

Section: License Plate Recognitionmentioning

confidence: 99%

Reading car license plates using deep neural networks

Wang

You

et al. 2018

Image and Vision Computing

159

198

View full text Add to dashboard Cite

Section: License Plate Recognitionmentioning

confidence: 99%

Reading car license plates using deep neural networks

Wang

You

et al. 2018

Image and Vision Computing

159

198

View full text Add to dashboard Cite

“…However, a major drawback of Gabor filters is that they are time consuming. Another popular method is wavelet transform and is based on small wavelets with limited duration [2,6,16]. In this method, vertical features are extracted using wavelet transform and the position parameters of the plate are determined by analysing the projection features in both the time and frequency domains.…”

Section: Related Workmentioning

confidence: 99%

“…The processing method in ALPR systems consists of three stages: license plate localisation (LPL), character segmentation, and optical character recognition (OCR) [5,6]. LPL scans all of the pixels within an image to detect and localise the position of a license plate.…”

Section: Introductionmentioning

confidence: 99%

Improved license plate localisation algorithm based on morphological operations

Yépez¹,

Ko²

2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.

show abstract

“…The proposed scheme utilizes a neural network chip named as CogniMem to detect the vehicle license plates. In [16], the authors propose a method using wavelet transform technique to decompose the images into different layers, and then utilize the low frequency images to combine with neural network technique.…”

Section: Introductionmentioning

confidence: 99%

IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities

2018

IEEE Internet Things J.

119

View full text Add to dashboard Cite

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for IoT and smart cities applications. In particular, smart vehicle license plate recognition (VLPR) and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems.

show abstract

A hybrid technique for License Plate Recognition based on feature selection of wavelet transform and artificial neural network

Cited by 18 publications

References 2 publications

Reading car license plates using deep neural networks

Reading car license plates using deep neural networks

Improved license plate localisation algorithm based on morphological operations

IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities

Contact Info

Product

Resources

About