臺灣博碩士論文加值系統

隨著經濟的起飛及商業活動的蓬勃發展，人們對於車輛的需求愈來愈多，雖然政府在交通建設方面已經非常普及，但在地稠人密的台灣地區，停車位不足的問題己是不爭的事實，因此，如何有效的管理停車場，提高停車位的利用率，則是我們關心的議題。
本論文所提出之車牌辨識系統，共分車牌定位、影像二值化、車牌矯正、字元切割及字元辨識等五大部分，在車牌定位部分，我們先以影像處理的技術，將輸入之車輛影像處理成固定解析度之灰階影像，再以Sobel Method的邊緣偵測法找出車牌的邊緣，最後以濾波器找出車牌位置；在影像二值化部份，我們利用動態門檻值法找出門檻值，將灰階車牌影像轉成二值化影像；在車牌矯正部分，我們利用車牌下輪廓分析法找出車牌傾斜的角度，進行矯正；在字元切割部分，我們利用垂直投影法找出字元的寛度，利用水平投影法將車牌的字元切割出來，最後，我們將切割完成之字元影像以部分辨識法的方式，將車牌影像中的車牌號碼辨識出來。
本系統擷取室內停車場及室外停車場共200張車牌影像，來進行車牌辨識的實驗，實驗結果在車牌定位方面成功率為98%，在字元切割方面成功率為95%，在字元辨識方面成功率為93%，每張影像辨識時間約需1.2秒。

Along with economical grow up and commerce activity vigorous development, people for the automobile need is more and more, although government for the traffic construction is very popular, but in the crowded Taiwan area, the question of parking space not enough is a fact of without saying, so how to manage parking lots efficiently and increasing usability of the parking lots that is our concerned question.
This thesis proposed the license plate recognition system, includes license plate locating, image binarization, calibration of license plate, character segmentation, character recognition and so on, total five parts; In the license plate locating, we use the image process technique to process the input image of automobile change into fixed resolution gray image, use again Sobel edge detection method to find out the edge of license plate, at last use filter to find out the position of license plate; In the image binarization, we use dynamic threshold value method to find out threshold value, let gray image of license plate change into binarized image; In the calibration of license plate, we use bottom outline of license plate analysis method to find out slope angle of license plate and to execute calibration; In the character segmentation, we use vertical projection method to find out the high of character, and we use horizontal projection method to segment the characters of license plate, at last we use partial recognition method to recognize the number of license plate image.
This system takes 200 license plate images from indoor and outdoor parking lots to execute the experiment of license plate recognition, experimental results, the license plate locating successful rate is 98%, the character segmentation successful rate is 95%, the character recognition successful rate is 93%, the average recognition time of each image needs 1.2 second.

ABSTRACT................................................................. i
摘要..................................................................... ii
誌謝..................................................................... iii
TABLE OF CONTENTS........................................................ iv
LIST OF FIGURES.......................................................... viii
LIST OF TABLES........................................................... xii
CHAPTER 1 ................................................................1
INTRODUCTION............................................................. 1
1.1 Motivation........................................................... 1
1.2 System Flow Chart Architecture....................................... 2
1.3 Thesis Organization.................................................. 5
CHAPTER 2................................................................ 7
LITERATURE RESEARCH...................................................... 7
2.1 Location of License Plate Research................................... 7
2.2 Character Segmentation Research...................................... 8
2.3 Character Recognition Research....................................... 9
CHAPTER 3 ................................................................11
LICENSE PLATE RECOGNITION SYSTEM RESEARCH ARCHITECTURE...................11
3.1 Location of License Plate............................................ 11
3.1.1 Image Process...................................................... 11
3.1.1.1 Image Capture.................................................... 11
3.1.1.2 Gray Level Image Process......................................... 12
3.1.1.3 Image Normalization ..............................................12
3.1.2 Edge Detection..................................................... 14
3.1.2.1 Sobel Edge Detection Theorem..................................... 14
3.1.2.2 Sobel Edge Detection Method...................................... 16
3.1.3 License Plate Locating............................................. 22
3.1.3.1 Vertical Projection Method....................................... 23
3.1.3.2 Horizontal Projection Method..................................... 24
3.2 Binarization of License Plate........................................ 26
3.2.1 The Fixed Binarization Method...................................... 27
3.2.2 The Average Binarization Method.................................... 27
3.2.3 Otsu’s Binarization Method........................................ 27
3.2.4 The Dynamic Binarization Method.................................... 29
3.2.4.1 Find Gray Level Average Value of License Plate................... 29
3.2.4.2 Find Threshold Value of License Plate Binarization............... 30
3.3 Calibration of License Plate......................................... 34
3.3.1 Find Slope Angle................................................... 34
3.3.1.1 Related Research in Slope Angle Calculation of License Plate..... 34
3.3.1.2 Slope Angle Calculation Theorem of License Plate................. 35
3.3.2 Calibration of Slope License Plate................................. 43
3.4 Character Segmentation of License Plate.............................. 45
3.4.1 Segmentation Method of Vertical Projector Volume................... 45
3.4.2 Segmentation Method of Horizontal Projection Volume................ 46
3.4.3 Character Normalization............................................ 48
3.5 Character Recognition of License Plate............................... 49
3.5.1 Sample Establish................................................... 50
3.5.2 Sample Analysis.................................................... 50
3.5.3 Character Matching................................................. 57
3.5.4 Character Recognition.............................................. 58
CHAPTER 4................................................................ 60
EXPERIMENT RESULTS....................................................... 60
4.1 Sample Source........................................................ 60
4.1.1 Sample Source of Related Research.................................. 60
4.1.2 Software and Hardware Architecture................................. 60
4.2 Experiment Process flow chart........................................ 61
4.2.1 License Plate Locating............................................. 62
4.2.1.1 Image Input...................................................... 62
4.2.1.2 Image Process.................................................... 62
4.2.1.3 Edge Detection................................................... 63
4.2.1.4 Find Position of License Plate................................... 63
4.2.2 Image Binarization................................................. 66
4.2.3 Calibration of License Plate....................................... 66
4.2.4 Character Segmentation............................................. 68
4.2.4.1 Vertical Projection Method....................................... 68
4.2.4.2 Horizontal Projection Method..................................... 69
4.2.4.3 Character Normalization.......................................... 69
4.2.5 Character Recognition ..............................................70
4.3 Experimental Results Analysis and Discussion......................... 75
4.3.1 Experimental Results Analysis...................................... 75
4.3.1.1 Indoor Parting Lots Car Image Experimental Results Analysis...... 75
4.3.1.2 Outdoor Parting Lots Car Image Experimental Results Analysis..... 75
4.3.1.3 All Experimental Results Analysis................................ 76
4.3.2 Experimental Results Disussion .....................................76
4.3.2.1 Failure Discussion about License Plate Locating.................. 76
4.3.2.2 Failure Discussion about Character Segmentation.................. 77
4.3.2.3 Failure Discussion about Character Recognition................... 77
CHAPTER 5................................................................ 79
CONCLUSIONS AND PROSPECTS................................................ 79
5.1 CONCLUSIONS.......................................................... 79
5.2 PROSPECTS............................................................ 80
REFERENCES............................................................... 82

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