臺灣汽機車密度名列世界前茅，但國內卻缺乏嵌入式汽機車牌照辨識系統之研究，本篇論文提出不需高階攝影機配合即能有效處理交通主要幹道汽機車牌照之即時多標的汽機車牌照辨識系統，解決一般攝影鏡頭動態影像來源之車輛牌照對比不足、髒污、歪斜狀況，並能運作在無DSP輔助之嵌入式系統上。

系統主要分為三個階段，分別為牌照定位、牌照處理與字元辨識，針對來源牌照影像不清之情形提出了「適應牌照之二值化門檻值尋找」方法來處理牌照，與常用二值化方法「區域Otsu」比較，本論文所提之方法於汽車牌照之系統辨識率提升了24.39%，在機車則提升了25.54%，在字元辨識的樣板比對部分利用字元輪廓特徵將樣板字元分為數個子集合，可有效避免6, 9, S之誤判，0, D, 8, B則透過統計左右輪廓外之背景像素比例來作為再次判斷的依據。

系統在交通主要幹道實際測試，汽機車牌照辨識率可達82.92%與80.85%，定位成功率可達96%以上，於嵌入式系統運作的效率在XScale-PXA270 624 Mhz / Linux環境下單一牌照時處理速度約可達到7 ~ 8 fps。

In Taiwan, density of vehicles is come out top in the world. However, it is short of embedded recognition system for license plates of cars and motorcycles in domestic study. In this paper, we proposed a DSP-less embedded system of Real-Time multi-target recognition without professional video camera to solve license plate images in conditions of low-contrast, blur, or skew.

The system is composed of three modules: localization, processing of vehicle plates, and character recognition. For the unclear vehicle plate images, we proposed a method which is suitable to binarize the license plate. Compared with localized Otsu’s method of 3 × 3 block processing, our method achieved 24.39% improvement for cars’ license plates recognition accuracy, and 25.56% improvement for motorcycles’ license plates. To improve template matching, we separated the character templates into some subsets by character’s outline, and could effectively avoid miss-recognition of the characters 6, 9, S. Moreover, by calculating the ratio of left and right side’s background pixels of character’s contour, the characters 0, D, 8, B could be authentically recognized.

The experimental videos were captured upon a sky bridge on Roosevelt Road, Taipei. The accuracies of car’s and motorcycle’s license plate recognition are 82.92% and 80.85%, and the accuracy of localization is 96%. The performance on embedded system (XScale-PXA270 624 MHz / Linux) is 7~8 fps.

[1] 何坤達，“嵌入式車牌辨識系統”，國立臺灣科技大學電子工程研究所碩士論，2006。
[2] 魏銪志，“動態多標的車牌辨識系統之研究”，元智大學資訊管理研究所碩士論文，2000。
[3] 莊佳龍，”車輛偵測與車牌辨識系統”，國立中正大學機電光整合工程研究所碩士論文，2005。
[4] 陳翔傑，”自動化車牌辨識系統設計”，國立中央大學電機工程研究所碩士論文，2005。
[5] 李正裕，”車牌辨識系統之研究”，靜宜大學資訊管理研究所碩士論文碩士論文，2003。
[6] 呂炎州，”不需字元切割的車牌辨識法”，靜宜大學資訊管理學研究所碩士論文，2003。
[7] 林泰良，”智慧型車牌定位與字串分割”，國立台灣大學電機工程研究所碩士論文，2000。
[8] 王振興，”多標的汽機車車牌辨識系統之研究”，元智大學資訊管理研究所碩士論文，2003。
[9] Clemens Arth, Florian Limberger, Horst Bischof, “Real-Time License Plate Recognition on an Embedded DSP-Platform,” Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, pp. 1-8.
[10] J. S. Kang, M. H. Kang, C. H. Park, J. H. Kim, Y. S. Choi, “Implementation of Embedded System for Vehicle Tracking and License Plates Recognition using Spatial Relative Distance,” Information Technology Interfaces, 2004. 26th International Conference on, vol.1, 2004, pp. 167-172.
[11] S.-Z. Wang and H.-J. Lee, “Detection and recognition of license plate characters with different appearances,” Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 2, 2003, pp. 979-984.
[12] S. Kim, D. Kim, Y. Ryu, and G. Kim, “A robust license-plate extraction method under complex image conditions,” Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 3, 2002, pp. 216-219.
[13] H. Bai, J. Zhu, and C. Liu, “A fast license plate extraction method on complex background,” Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 2, 2003, pp. 985-987.
[14] H. Bai and C. Liu, “A hybrid license plate extraction method based on edge statistics and morphology,” Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2, 2004, pp. 831-834.
[15] V. Kamat and S. Ganesan, “An efficient implementation of the Hough transform for detecting vehicle license plates using DSP’S,” Real-Time Technology and Applications Symposium, 1995. Proceedings, 1995, pp. 58-59.
[16] S. Gendy, C. L. Smith, and S. Lachowicz, “Automatic car registration plate recognition using fast Hough transform,” Security Technology, 1997. Proceedings. The Institute of Electrical and Electronics Engineers 31st Annual 1997 International Carnahan Conference on, 1997, pp. 209-218.
[17] Jianlong Zhu, Yannan Zhao, “Vehicle license image segmentation using wavelettransform,” Intelligent Multimedia, Video and Speech Processing, 2001. Proceedings of 2001 International Symposium on, 2001, pp. 267–270.
[18] Ching-Tang Hsieh, Yu-Shan Juan, Kuo-Ming Hung, “Multiple license plate detection for complex background,” Advanced Information Networking and Applications, 2005. AINA 2005. 19th International Conference on, vol. 2, 2005, pp. 389-392.
[19] Huiqiong Chen, Derek Rivait and Qigang Gao, “Real-Time License Plate Identification by Perceptual Shape Grouping and Tracking,” Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE, pp.1352-1355.
[20] W. Wei, M. Wang, and Z. Huang, “An automatic method of location for number-plate using color features,” Image Processing, 2001. Proceedings. 2001 International Conference on, vol. 1, 2001, pp. 782-785.
[21] S. H. Park, K. I. Kim, K. Jung, and H. J. Kim, “Locating car license plates using neural networks,” Electronics Letters, vol. 35, 1999, pp. 1475-1477.
[22] S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithm based segmentation,” Image Processing, 1996. Proceedings, International Conference on, vol. 1, 1996, pp. 661-664.
[23] Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen, Automatic license plate recognition,” Intelligent Transportation Systems, IEEE Transactions on, vol. 5, Issue 1, 2004, pp. 42-53.
[24] Acharya, Tinku / Ray, Ajoy K, Image Procesing, John Wiley & Sons, 2005, pp.135-136
[25] N. Otsu, “A Threshold Selection Method from Gray-Level Historgams,” IEEE Transactions on System, Man, and Cybernetics, vol. SMC-9, 1979, pp. 62-66.
[26] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd Edition, Prentice-Hall, New Jersey, 2002
[27] J. R. Parker, Algorithms for Image Processing and Computer Vision, Wiley; Pap/Cdr edition, November 25, 1996
[28] Naito, T.; Tsukada, T.; Yamada, K.; Kozuka, K.; Yamamoto, S, “Robust license-plate recognition method for passing vehicles under outside environment,” Vehicular Technology, IEEE Transactions on, vol. 49, Issue 6, 2000, pp. 2309-2319
[29] Hongliang Bai; Junmin Zhu; Changping Liu , “A fast license plate extraction method on complex background,” Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, vol. 2, 2003, pp. 985-987
[30] Naito, T.; Tsukada, T.; Yamada, K.; Kozuka, K.; Yamamoto, S, “Robust recognition methods for inclined license plates under various illumination conditions outdoors,” Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on, 1999, pp. 697-702
[31] The Ångström Distribution, http://www.angstrom-distribution.org/
[32] Open Source Computer Vision Library, http://www.intel.com/technology/computing/opencv/