跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 陳克智
Ko-Chih Chen
論文名稱： 照相手機的車牌偵測與辨識
License Plate Detection and Recognition of Smart-Phone
指導教授： 曾定章
Din-Chang Tseng
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系在職專班
Executive Master of Computer Science & Information Engineering
畢業學年度： 99
語文別： 中文
論文頁數： 85
中文關鍵詞： 區域二值化 、仿射轉換 、車牌辨識 、車牌傾斜 、車牌陰影
外文關鍵詞： LPR shadow, LPR skew, local threshold, LPR recognition, affine transformation
相關次數： 點閱：8  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 在本論文中，我們提出照相手機的車牌偵測與辨識系統，用以解決車牌歪斜及車牌陰影造成的文字影像分割及辨識問題，提供一個可靠與準確的車牌偵測與辨識技術。我們所提出的方法有四個階段：分別為車牌定位、車牌校正，文字分割及文字辨識。
    針對車牌定位，我們分析影像邊界點的垂直與水平方向累積量，利用邊界點累積量的數量與位置特徵分割車牌影像。車牌校正方面，我們使用區域二值化法，克服車牌陰影問題，並分析二值化影像中的白色與黑色像素點數量藉以作為判斷影像反相依據，接著利用車牌文字連結區塊bounding box的長寬比、大小以及其位置等特徵刪除非車牌區域；由於車牌傾斜的影像存在旋轉、歪斜問題，故我們將利用仿射轉換中的旋轉、歪斜轉換法，校正傾斜的車牌。
    文字分割方面，我們利用邊界點垂直方向累積量與波谷分割文字，並將文字大小正規化至40×90。最後我們利用正規化相關係數樣板比對法作為文字辨識的方法，為了縮短比對時間，我們改變車牌文字辨識的程序，首先將樣板與待測影像尺寸縮小為1/4進行第一次的樣板比對，在比對結果中選取3個分數最高，進行最終的原尺寸 (40×90) 樣板比對程序，分數最高者即為所選。

    This paper presents an approach for license plate recognition using a camera-equipped smartphone. The proposed method provides a reliable and accurate technique to solve the problem of license plate recognition caused by the skew and shadow on the license plates. There are four stages in the proposed approach: license plate location, license plate rectification, character segmentation and character recognition.
    In the first stage, we locate the license plate by accumulating edge points, and then analyze the edge points and accumulation associated with vertical and horizontal dimensions of the image. As to the second stage, license plate rectification, we adopt local threshold to cope with the problem of shadow on the plates first. Next step involved analyzing black and white pixels in order to decide whether to invert the image or not. The researcher tries to engage the characteristics like length-width ratio, size, and position of the bounding box in the text region to eliminate the non-text portions. To solve the rotation, skew, and scale problems of the slanted license plates in the image, we use an affine transformation to estimate the skew angle.
    Edge points vertical direction accumulating and trough are used to segment characters section in the third stage. We normalize the characters size to 40 × 90. Finally, criterion of normalized cross-correlation is used in the last stage for character recognition. In behalf of shortening the process time for identification, the procedure of character reorganization is improved. We shrink the samples to one-fourth the size to conduct the first identification process. Then, three highest-coefficient samples are chosen to match the original input pattern. From these three samples, the highest-coefficient one is selected as the final result.

    摘　要 ......................................................... i 誌　謝 ......................................................... iii 目　錄 ......................................................... iv 圖目錄 ......................................................... vii 表目錄 ......................................................... x 第一章 　緒論　................................................. 1 1.1 研究背景與動機　....................................... 1 1.2 系統流程　............................................. 3 1.3 論文架構　............................................. 6 第二章 　相關研究探討　......................................... 8 2.1 車牌定位　............................................. 8 2.2 車牌文字分割　......................................... 12 2.3 車牌文字辨識　......................................... 13 2.3.1 分類器分類法　.................................. 13 2.3.2 統計分類法　.................................... 15 2.3.3 樣板比對法　.................................... 16 2.4 台灣車牌種類　......................................... 16 第三章 　車牌定位方法　......................................... 19 3.1 影像前處理　........................................... 21 3.1.1 影像格式轉換　.................................. 21 3.1.2 直方圖均勻化　.................................. 23 3.1.3 中值濾波器　.................................... 25 3.2 車牌影像邊界點偵測　................................... 25 3.3 車牌定位　............................................. 29 3.3.1 資料數值精簡　.................................. 29 3.3.2 水平投影　...................................... 30 3.3.3 垂直投影　...................................... 31 第四章 　車牌文字分割方法　..................................... 33 4.1 影像二值化............................................. 34 4.1.1 全域二值化法　.................................. 35 4.1.2 區域二值化法　.................................. 35 4.1.2.1 Niblack演算法............................ 36 4.1.2.2 Sauvola演算法............................ 36 4.1.2.3 Bernsen演算法............................ 36 4.1.2.4 各種區域二值化法優缺點比較............... 37 4.1.3 車牌影像反白　.................................. 38 4.1.4 連結區塊標記　.................................. 39 4.2 文字區域分割　......................................... 40 4.3 傾斜車牌文字分割　..................................... 41 4.3.1 車牌傾斜種類　.................................. 41 4.3.2 傾斜車牌文字分割　.............................. 42 4.4 車牌分類　............................................. 44 第五章 　文字辨識　............................................. 46 5.1 樣板資料庫的建立　..................................... 48 5.2 文字辨識步驟　......................................... 49 第六章 　實驗結果　............................................. 50 6.1 實驗環境　............................................. 50 6.2 車牌定位　............................................. 51 6.2.1 不同時間點車牌定位　............................ 51 6.2.2 複雜背景之車牌定位　............................ 52 6.2.3 陰影車牌之車牌定位　............................ 53 6.2.4 髒污車牌之定位　................................ 54 6.2.5 板彎車牌之定位　................................ 55 6.2.6 遮蔽車牌之定位　................................ 56 6.2.7 傾斜車牌之定位　................................ 57 6.2.8 車牌定位效果分析　.............................. 58 6.3 車牌文字區塊分割　..................................... 60 6.4 車牌字元辨識　......................................... 62 第七章 　結論　................................................. 64 參考文獻　...................................................... 66

    [1] Anagnostopoulos, C., E. Kayafas, and V. Loumos, "Digital image processing and neural networks for vehicle license plate identification," in Proc. 7th Int. Conf. Optimization of Electrical and Electronic Equipment, Brasov, Romania, May.10-12, 2000, pp.787-792.
    [2] Anagnostopoulos, C., I. Anagnostopoulos, V. Loumos, E. Kayafas, “A license plate recognition system for intelligent transportation system applications,” IEEE Trans. Intelligent Transportation Systems, vol.7, no.3, pp.377-392, Sep. 2006.
    [3] Anagnostopoulos, C., I. Anagnostopoulos, I. Psoroulas, V. Loumos, and E. Kayafas, “License plate recognition from still images and video sequences: a survey,” IEEE Trans. Intelligent Transportation Systems, vol.9, no.3, pp.377-391, Sep. 2008.
    [4] Bai, H. and C. Liu, “A hybrid license plate extraction method based on edge statistics and morphology,” in Proc. 17th Int. Conf. on Pattern Recognition, Hangzhou, China, Aug.23-26, 2004, pp.831-834.
    [5] Bellas, N., S. M. Chai, M. Dwyer, and D. Linzmeier, "FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators," in Proc. 20th IPDPS, Rhodes Island, Apr.25-29, 2006, pp.8-15.
    [6] Bernsen, J., “Dynamic thresholding of grey-level images,” in Proc. 18th Int. Conf. Pattern Recognition, Paris, 1986, pp.1251-1255.
    [7] Broumandnia, A. and M. Fathy, "Application of pattern recognition for farsi license plate recognition," Int. Journal on Graphics, Vision and Image Processing, vol.5, no.2, pp.25-31, Jan. 2005,
    [8] Canny, J., A computational approach to edge detection, IEEE Tran. on Patt. Anal. and Machine Intell., vol. 8, no. 6, pp. 679-698.
    [9] Cao, J., M. Ahmadi, and M. Shridhar, "Recognition of handwritten numerals with multiple feature and multistage classifier," Pattern Recognition, vol.2, no.28, pp.153-160, Feb. 1995.
    [10] Capar, A. and M. Gokmen, "Concurrent segmentation and recognition with shape-driven fast marching methods," in Proc. 18th Int. Conf. on Pattern Recognition, Hong Kong, Aug.20-24, 2006, vol.1, pp.155-158.
    [11] Chang, S.-L., L.-S. Chen, Y.-C. Chung, and S.-W. Chen, "Automatic license plate recognition," IEEE Trans. Intelligent Transportation Systems, vol.5, no.1, pp.42-53, Mar. 2004.
    [12] Comelli, P., P. Ferragina, M. N. Granieri, and F. Stabile, "Optical recognition of motor vehicle license plates," IEEE Trans. Vehicular Technology. vol.44, no.4, pp.790-799, Nov. 1995.
    [13] Donald F.-S., Journal Neural Networks, vol.3, Issue 1, 1990, Elsevier Science Ltd. Oxford, UK.
    [14] Draghici, S., "A neural network based artificial vision system for license plate recognition," Int. Journal of Neural Systems, vol.8, no.1, pp.113-126, Feb. 1997.
    [15] Duan, T.-D., T.-L. Du, T.-V. Phuoc, and N.-V. Hoang, "Building an automatic vehicle license-plate recognition system," in Proc. Int. Conf. Computer Science, Can Tho, Vietnam, Feb.21-24, 2005, pp.59-63.
    [16] Gao, Q., X. Wang, and G. Xie, “License plate recognition based on prior knowledge,” in Proc. Int. Conf. on Automation and Logistics, Jinan, China, Aug.18-21, 2007, pp.2964-2968.
    [17] Han, P., H. Wei, D.-F. Wang, and Y.-J. Zhai, "Car license plate feature extraction and recognition based on multi-stage classifier," in Proc. Int. Conf. on Machine Learning and Cybernetics, Hebei, China, Nov.2-5, 2003, pp.128-132.
    [18] Hegt, H. A., R. J. Haye, and N. A. Khan, “A high performance license plate recognition system,” in Proc. Int. Conf. on Systems, Man, and Cybernetics, San Diego, CA, Oct.11-14, 1998, pp.4357-4362.
    [19] Hsieh, C.-T., Y.-S. Juan, and K.-M. Hung, "Multile license plate detection for complex background," in Proc. 19th Int. Conf. on Advance Iinformation Networking and Applications, Taipei, Taiwan, Mar28-30, 2005, pp.389-392.
    [20] Huang, L.-K. and M.-J. Wang, “Image thresholding by minimizing the measures of fuzziness,” Pattern Recognition, vol.28, no.1, pp.41-51, Jan. 1995.
    [21] Huang, Y.-P., S.-Y. Lai, and W.-P. Chuang, "A template-based model for license plate recognition," in Proc. IEEE Int. Conf. on Networking Sensing and Control, Taipei, Taiwan, Mar. 21-23, 2004, vol.2, pp.737-742.
    [22] Jung, C. R. and R. Schramm, "Rectangle detection based on a windowed Hough transform," in Proc. 17th Brazilian Symp. on Computer Graphics and Image Processing, Leopoldo, Brasil, Oct.17-20, 2004, pp.113-120.
    [23] Kahraman, F., B. Kurt, and M. Gökmen, “License plate character segmentation based on the Gabor transform and vector quantization,” Lecture Notes on Computer Science, vol. 2869, pp. 381-388, 2003.
    [24] Kamat, V. and S. Ganesan, "An efficient implementation of the Hough transform for detecing vehicle license plates using DSP''S," in Proc. Conf. Real-Time Technology and Application., Chicago, IL, May 15-17, 1995, pp.58-59.
    [25] Kim, K. K., K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Proc. of Workshop on Neural Networks for Signal Processing, Sydney, Australia, Dec.11-13, 2000, pp.614-623.
    [26] Kittler, J., M. Hatef., and R. P. W. Duin, "On combining classifiers," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.20, no.3, pp.226-239, Mar. 1998.
    [27] Kwasnicda, H. and B. Wawrzyniak, License Plate Localization and Recognition in Camera Pictures, Ph.D. dissertation, Faculty Division of Computer Science, Wroclaw Univ. of Technology, Wrocław, Poland, 2002.
    [28] Lee, E. R., P. K. Kim, and H. J. Kim, "Automatic recognition of a car license plate using color image processing," in Proc. IEEE Int. Conf. Image Processing, Austin, TX, Nov.13-16, 1994, pp.301-305.
    [29] Lee, H.-J., S.-Y. Chen, and S.-Z. Wang, "Extraction and recognition of license plates of motocycles and vehicles on highways," in Proc. 17th Int. Conf. Pattern Recognition, Hsinchu, Taiwan, Aug.23-26, 2004, pp.356-359.
    [30] Lu, Y., "On the segmentation of touching characters," in Proc. 2nd Int. Conf. on Document Analysis and Recognition, Tsukuba Science City, Japan, Oct.20-22, 1993, pp.440-443.
    [31] Mei, Y. and Y. D. Kim, “An approach to Korean license plate recognition based on vertical edge matching,” in Proc. Int. Conf. on Systems, Man, and Cybernetics, Nashville, TN, Oct.8-11, 2000, pp.2975-2980.
    [32] Niblack, W., An introduction to Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1986, pp.115-116.
    [33] Nomura, S., K. Yamanaka, O. Katai, and H. Kawakami, "A new method for degraded color image binarization based on adaptive lightning on grayscale versions," IEICE Trans. Information and Systems, vol.E87-D, no.4, pp.1012-1020, Apr. 2004.
    [34] Otsu, N., “A thresholding selection method from gray level histograms,” IEEE Trans. Systems Man and Cybernetics, vol.9, pp.62-66, Jan. 1979.
    [35] Pan, X., X. Ye, and S. Zhang, "A hybrid method for robust car plate character recognition," in Proc Int. Conf. on Systems Man and Cybernetics, Hague, Netherlands, Oct.10-13, 2004, pp.4733-4737.
    [36] Sauvola, J. and M. Pietikainen, “Adaptive Document Image Binarization, “ Pattern Recognition, vol.33, pp.225-236, 2000.
    [37] Sethian, J. A., A fast marching level set method for monotonically advancing fronts, in Proc. National Academy of Sciences of America, Berkeley, CA, Feb.20, 1996, vol.93, no.4, pp.1591-1595.
    [38] Shi, X., W. Zhao, and Y. Shen, “Automatic license plate recognition system based on color image processing,” Computational Science and ITS Applications (ICCSA), vol.3483, pp.1159-1168, 2005.
    [39] Suryanarayana, P. V., S. K. Mitra, A. Baneree, and A. K. Roy, "A morpology based approach for car license plate extraction," in Proc. IEEE India Conf. (INDICON), Chennai, India, Dec.11-13, 2005, pp.24-27.
    [40] Trier, O. D., A. K. Jain, and T. Taxt, "Feature extraction methods for character recognition - a survey," Pattern Recognition, vol.29, no.4, pp.641-662, Apr. 1996.
    [41] Wang, S.-Z., and H.-J. Lee, “Detection and recognition of license plate characters with different appearances,” in Proc. Conf. Int. Trans. Systems, 2003., vol.2, pp.979-984, Oct. 2003.
    [42] Wang, T.-H., F.-C. Ni, K.-T. Li, and Y.-P. Chen, "Robust license plate recognition based on dynamic projection warping," in Proc. Conf. IEEE Int. Conf. Networking Sensing and Control, vol.2, pp.784-788, Sep. 2004,
    [43] Xu, L., A. Krzyzak, and C. Y. Suen, "Methods of combining multiple classifiers and their application to handwriting recognition," IEEE Trans. Systems Man and Cybernetics, vol.22, no.3, pp.418-435, Jun. 1992.
    [44] Yan, D., H. Ma, J. Liu, and L. Li, “A high performance license plate recognition system based on the web technique,“ in Proc. Conf. Intelligent Transportation Systems, Oakland, CA, Aug.25-29, 2001, vol.5, no.1, pp.325-329.
    [45] Zhang, H., W. Jia, X. He, and Q. Wu, "Learning-based license plate detection using global and local features," in Proc. 18th Int. Conf. Pattern Recognition, Hong Kong, Sep.18, 2006, vol.2, pp.1102-1105.
    [46] Zhang, P. and L.-H. Chen, "A novel feature extraction method and hybrid tree classification for handwritten numeral recognition," Pattern Recognition Letters, vol.23, no.1-3, pp.45-56, Jan. 2002.
    [47] Zhang, Y. and C. Zhang, “A new algorithm for character segmentation of license plate,” in Proc. Int. Conf, on Intelligent Vehicles Symp., Beijing, China, Jun.9-11, 2003, pp.106-109.
    [48] Zheng, D., Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognition Letters, vol.26, no.15, pp.2431-2438, Nov. 2005.
    [49]交通部公路總局網站公路總局常見問答集-車輛管理作業問答集http://www.thb.gov.tw/TM/Menus/Menu03/main0308-2.aspx

    QR CODE
    :::