臺灣博碩士論文加值系統

本論文是以可在不限制任何環境的即時系統，並且能快速且準確的辨識出車牌為前提，提出即時車牌自動辨識系統。之前也有許多相關類似的系統被提出，如在固定或單純背景的情況、一定亮度下的環境、部份車牌的辨識等，大部份的系統都是採用辨識圖片的方式，而我們現今所提的系統是可辨識在一定角度和大小範圍內所有種類的車牌，其背景和光源是沒有任何限制的，並且在以即時的方式來做辨識。我們所採用的方式主要分成三大步驟：即時影像截取、車牌定位與車牌辨識。在即時影像的部份是採用現有且通用的截取方式，以每秒28張圖片來做資料的匯入，但考慮到資料處理的速度和影像的重覆性，故以每兩張才進行一次處理，所以處理的速度約為每秒14張影像。在車牌定位的部份，先將彩色影像轉成灰階，再利用型態學的侵蝕與擴張的方式和一些判斷條件，如此可以不考慮光源和顏色的影響來找出影像中所有類型車牌的位置。在辨識的部份，採用類神經網路結合模糊理論來提高其辨識率。本論文的重點部份在於藉由提高車牌定位的成功率，來增加車牌的辨識率，經實驗的結果得到滿意的辨識率，証明此系統的可行性。

A real-time automatic license plate recognition system is proposed in this paper to identify the license plate quickly and accurately in the unrestricted environment, e.g. different lighting conditions and various vehicles. The proposed system mainly includes two procedures: license plate locating and character recognition. In license plates locating procedure, we convert the color image into grey level data first. Then apply the morphology technique to find out the location of license plate. The objective of the research is to increase the recognition rate of license plates and to improve the success rate of license plate locating. We demonstrate the feasibility of this system of this system through extensive experiments. The correct plate location rate and characters recognition achieves 96.63% and 93.20%, respectively.

1 Introduction and Literature Survey 1
1.1 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3.1 License Plate Locating . . . . . . . . . . . . . . . . . . 3
1.3.2 Character Recognition . . . . . . . . . . . . . . . . . . 4
1.4 Guide of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 5
2 System Architecture 6
3 License Plate Location 9
4 License Plate Recognition 16
4.1 Characters Segmentation . . . . . . . . . . . . . . . . . . . . . 16
4.2 Back Propagation Neural Network . . . . . . . . . . . . . . . . 19
4.2.1 BPNN Structure . . . . . . . . . . . . . . . . . . . . . 19
4.2.2 Back Propagation Learning Algorithm . . . . . . . . . 23
4.3 Character Recognition . . . . . . . . . . . . . . . . . . . . . . 24
5 Experimental Results 28
6 Conclusion and Future Work 34

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