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    Title: 多車牌辨識系統之研究
    Other Titles: A study of multiple vehicle license plates recognition system
    Authors: 梁智凱;Liang, Chih-kai
    Contributors: 淡江大學電機工程學系碩士班
    謝景棠;Hsieh, Ching-tang
    Keywords: 車牌偵測;字元切割;車牌辨識;類神經網路;License Plate Detection;Character Segmentation;License Plate Recognition;Neural Network
    Date: 2006
    Issue Date: 2010-01-11 07:10:59 (UTC+8)
    Abstract: 本論文提出一個多車牌辨識系統的架構設計,在一張影像中搜尋一個以上的車牌正確位置並將車牌上的每個字元獨立切割出來,最後再利用類神經方法辨識出車牌上的正確資訊。

    在本文所提出的方法中,首先我們使用對比增強的前處理,使字元與車牌底色的對比度提高,因而增加定位車牌的準確性。並利用邊緣角度以及型態學的擴張等方法來快速去除複雜背景,並以車牌具有對稱特性找出車牌候選區,將具有車牌字元的候選區定義為車牌區域。然後再使用標示連通物件的方法找出每個字元所形成的斜率,並與水平線作為基準所形成的夾角,視為車牌傾斜的角度,並依照所傾斜的角度扭正車牌,之後將每個標示字元依次分割出來。

    在車牌字元辨識部份,我們利用類神經網路的方法來辨識字元。由於類神經網路是使用大量的神經元來模仿生物神經網路的能力,並且可以透過學習的方式,來解決資料分類的問題,因此可以使用類神經網路的方法來解決辨識車牌字元的問題。除此之外,類神經網路還具有高容錯性,這個特性有利於解決切割出的字元具有雜訊或者影像殘缺不全的問題。

    本研究分別以正面拍攝之單車輛與多車輛影像作為測試,拍攝環境為白天、晚上、晴天與陰天。其中單車輛拍攝了329張照片,而多車輛影像拍攝了141張287面的車牌。此外也對36部機車每部分別以7種不同角度、2種不同距離拍攝14張影像,共504張影像,以評估系統的效能。
    In this paper we propose a structure and design of a multiple vehicle license plates recognition system. It can search more than one correct positions of plate in an image and cut out each single word on the plates and then utilize neural network to distinguish out the information on the plates.

    In the proposed method, we use preprocess of contrast enhancement to improve the accuracy of plate location. Then we use the edge angle and morphological method to get rid of the complicated background, and utilize the symmetrical characteristics to find out the plate and define the area including characters as plate locations.

    Then, we use connected-component analysis to find out the slope of plate and rotate the plate according to the angles sloped, later cut out each labeled character sequentially.

    In the recognition, we use the neural network because of the ability that a large number of neurons imitate the neural network of living beings. Besides, neural network has high and getting fault-tolerant, and that is helpful to against the noisy or incomplete cut characters.

    This research is tested with the inputted images conduct of single vehicle and multi vehicles, shot in the evening, fine and cloudy day. And there are 329 single vehicle images and 141 multi vehicles images, totaling 287 plates. In order to test system efficiency, we also add the plates which are shot under on viewable angles.
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Thesis

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