臺灣博碩士論文加值系統

車牌辨識技術在智慧型運輸系統的應用上扮演著相當關鍵的角色，可應用於停車管控、贓車追緝、電子收費、車輛檢驗等。本論文提出一套低運算量、高辨識率的車牌辨識方法，首先實現了車牌辨識系統的軟體開發以評估車牌辨認方法的性能。整個處理過程分為前處理、車牌定位、字元切割、字元識別等區塊。在實現時，我們對出現的問題進行具體分析、並提出較佳的解決辦法。
其中，我們發現不均勻光線是影像處理的一大困難，造成車牌辨識失敗率的大幅增加。因此我們將車牌影像分成若干方塊，因為方塊內的照明均勻性較高，接著對每一方塊作otsu二值化。
使用本論文的方法，在100張不均勻光線的車牌，辨識率可以由原本的64%提升到86%，說明本文所提出的方塊法確實可以解決不均勻光線下車牌辨識的問題。

The license plate recognition plays a key role in intelligent transport systems,applied to parking control, looking for stolen cars, electronic toll collection, vehicle inspection. This paper presents a low computational , high recognition of license plate recognition method. First license plate recognition system development methods to assess the license plate to identify performance. The whole process is divided into pretreatment, the license plate location, character segmentation, character recognition block. In achieving a specific analysis of problems, and propose a better solution.
Among them, we found that the non-uniform Illumination is a difficult image processing, resulting in a substantial increase in license plate recognition failure rate. Therefore, we the license plate image is divided into a square blocks.
In this paper, in 20 non-uniformity of Illumination license plate recognition rate can be increased by 70% to 95%, indicating that the proposed square blocks method can really solve the problem of license plate recognition in the non - uniform light.

目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章：緒論 1
1.1前言 1
1.2 研究背景動機與目標 2
1.3 本文大綱 2
第二章 文獻回顧 4
2.1國內外相關研究 4
2.2車牌定位相關文獻 4
2.3 車牌字元分割相關文獻 6
2.4 車牌文字辨識相關文獻 7
2.5影像二值化相關文獻 9
第三章 一般性車牌辨識平台實現與評估 19
3.1色彩轉換 20
3.2車牌定位 22
3.2.1邊緣偵測 22
3.2.2型態學 23
3.2.3篩選車牌區域 25
3.3字元切割 27
3.3.1水平投影 27
3.3.2垂直投影 28
3.4正規化. 29
3.5樣版比對 30
第四章 研究方法 32
4.1不均勻光線下車牌辨識所面臨的問題 32
4.2方塊為基礎之不均勻光線下的車牌辨識 33
4.3不均勻光線下車牌使用測試影像 35
4.4方塊切割 38
4.5方塊大小之探討 40
4.6基於方塊法與otsuMAT及Niblack比較圖 40
4.7不均勻光線下車牌辨識實驗結果 41
4.8圖片大小對執行速度比較 45
4.9 100組不均勻光線車牌實驗設計 46
第五章 結論 49
5.1本論文貢獻與發現 49
5.2未來研究發展 49
附錄 50
References 54

[1] Broumandnia, A. and M. Fathy, “Application of pattern recognition for farsi license plate recognition,” in Proc. of Int. Conf. Graphics, Vision and Image Processing, Cairo, Egypt, Dec.19-21, 2005, vol.5, pp.25-31.

[2] Brugge, M. H. Ter, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in Proc. of 5th IEEE Int. Workshop on Cellular Neural Networks and Their Applications Proceedings, London, UK, Apr.14-17, 1998, pp.212-217.

[3] Comelli, P., P. Ferragina, M. N. Granieri, and F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Trans. on Vehicular Technology, vol.44, no.4, pp.790-799, Nov. 1995.

[4] Draghici, S., “A neural network based artificial vision system for license plate recognition,” Int. Journal of Neural Systems, vol.8, pp.113-126, Feb. 1997.

[5] Gao, Q, X. Wang, and G. Xie, “License plate recognition based on prior knowledge,” in Proc. of IEEE Int. Conf. on Automation Logistics, Shandong, China, Aug.18-21, 2007, pp.2964-2968.

[6] Hegt, H., R. de la Haye, and N. Khan, “A high performance license plate recognition system,” in Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics, Tokyo, Japan, Oct.12-15, 1999, vol.5, pp.4357-4362.

[7] Hussain, B. and M. R. Kabuka, “A novel features recognition neural network and its applications to character recognition,” IEEE Trans.Pattern Analysis and Machine Intelligence, vol.16, no.1, pp.98-106, Jan.1994.

[8] Juntanasub, R. and N. Sureerattanan, “Car license plate recognition through hausdorff distance technique,” in Proc. of IEEE 17th Int. Conf.on Tools with Artificial Intelligence, Hong Kong, China, Nov.14-16,2005, pp.647-651.

[9] Kertesz, A., V. Kertesz, and T. Muller, “An on-line image processing system for registration number identification,” in Proc. of IEEE Int. Conf. on Neural Networks, Orlando, Florida, June 27-29, 1994, vol.6, pp.4145-4148.

[10] Lisa, F., J. Carrabina, C. Perez-Vicente, N. Avellana, and E. Valderrama, “Two-bit weights are enough to solve vehicle license number recognition problem,” in Proc. of IEEE Int. Conf. on Neural Networks, San Francisco, California, Jul.10-16, 1993, vol.3, pp.1242-1246.

[11] Lu, Y., “Machine printed character segmentation - an overview,” Pattern Recognition, vol.28, no.1, pp.67-80, Jan. 1995.

[12] Morns, I. P. and S. S. Dlay, “Character recognition using fourier descriptors and a new form of dynamic semi-supervised neural network,” Microelectronic Journal, vol.28, no.1, pp.73-84, Jan. 1997.

[13] Naito, T., T. Tsukada, K. Yamada, K. Kozuka, and S. Yamamoto, “Robust license-plate recognition method for passing vehicles under outside environment,” IEEE Trans. on Vehicular Technology, vol.49, no.6, pp.2309-2319, Nov. 2000.

[14] Parisi, R., Di Claudio, E.D., G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in Proc. of IEEE Int. Symp. on Circuits and Systems, Monterey, California, May 31-June 3, 1998, vol.3, pp.195-198.

[15] Rahman, C. A., W. Badawy, and A. Radmanesh, “A real time vehicle’s license plate recognition system,” in Proc. IEEE Conf. on Advanced Video and Signal Based Surveillance, Miami, FL, Jul.21-23, 2003, pp.163-166.

[16] Raus, M. and L. Kreft, “Reading car license plates by the use of artificial neural networks,” in Proc. of the 39th Midwest Symp. on Circuits and Systems, Ames, Iowa, Aug.18-21, 1996, vol.1, pp.538-541.

[17] Tindall, D. W., “Application of neural network techniques to automatic license plate recognition,” in Proc. of IEE European Convention on Security and Detection, Brighton, UK, May 16-18, 1995, pp.81-85.

[18] Otsu, N., “A threshold selection method from gray-level histograms,” IEEE Trans. on Systems, Man, and Cybernetics, vol.9, no.1, pp.62-66, Jan. 1979.

[19]W.Niblack, “An introduction to digital image processing,” Prentice-Hall, Englewood Cliffs,NJ, 1986 pp.115-116

[20]Coetzee, C.,Botha, C., and Weber, D., “PC Based Number Plate Recognition System,” Proceedings of IEEEInternational Symposium on Industrial Electronics, Vol. 2, pp. 605-610 (1998).

[21]Feixiang Yan, Hong Zhang, C. Ronald Kube, “A multistage adaptive thresholding method,’’ Pattern Recognition Letters 26, pp.1183–1191,2005.