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Automatic License Plate Recognition ...

Fu, Yuanxi.

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Automatic License Plate Recognition Using Neural Network and Signal Processing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automatic License Plate Recognition Using Neural Network and Signal Processing./
作者:	Fu, Yuanxi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	44 p.
附註:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13808977
ISBN:	9781392170779

Automatic License Plate Recognition Using Neural Network and Signal Processing.
Fu, Yuanxi.

Automatic License Plate Recognition Using Neural Network and Signal Processing. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 44 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.)--University of California, Riverside, 2019.

This item must not be added to any third party search indexes.

Automatic Plate Recognition plays an important roll in intelligent transportation systems. However, most license plate recognition methods work under restricted conditions like slow speed and good illumination. That is a restriction on industrial application. In this thesis, the constraints are relaxed by vanished points distortion-recovery method and denoising method. This thesis implements a license plate recognition method by morphological edge detection method and convolution neural network recognition method. The thesis is constructed contributes to several papers optimization methods. The proposed approach can be trained for recognition of country-specific license plates. More than 500 images are collected for training and over 300 images are collected for recognition test. This paper achieves 97.05% on license plate recognition for detecting total characters and numbers of the license plates. License plate recognition consists three parts, pre-processing image, locating license plate and identifying license numbers and characters. License plate location is important to obtain license images and plays a key role in identifying plates. The plate recognition has two major steps, character separation and identification. In this paper, machine learning method is applied for license plate recognition.

ISBN: 9781392170779Subjects--Topical Terms:

1567821
Computer Engineering.

Automatic License Plate Recognition Using Neural Network and Signal Processing.
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500 $a Source: Masters Abstracts International, Volume: 80-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Hua, Yingbo.
502 $a Thesis (M.S.)--University of California, Riverside, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Automatic Plate Recognition plays an important roll in intelligent transportation systems. However, most license plate recognition methods work under restricted conditions like slow speed and good illumination. That is a restriction on industrial application. In this thesis, the constraints are relaxed by vanished points distortion-recovery method and denoising method. This thesis implements a license plate recognition method by morphological edge detection method and convolution neural network recognition method. The thesis is constructed contributes to several papers optimization methods. The proposed approach can be trained for recognition of country-specific license plates. More than 500 images are collected for training and over 300 images are collected for recognition test. This paper achieves 97.05% on license plate recognition for detecting total characters and numbers of the license plates. License plate recognition consists three parts, pre-processing image, locating license plate and identifying license numbers and characters. License plate location is important to obtain license images and plays a key role in identifying plates. The plate recognition has two major steps, character separation and identification. In this paper, machine learning method is applied for license plate recognition.
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