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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35803

    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。



    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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