智慧型手機的車牌辨識系統;Smartphone License Plate Recognition System

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题名:	智慧型手機的車牌辨識系統;Smartphone License Plate Recognition System
作者:	曾建豪;Tseng,Chien-hao
贡献者:	資訊工程學系碩士在職專班
关键词:	車牌辨識;智慧型手機;license plate recognition;smartphone
日期:	2012-07-13
上传时间:	2012-09-11 18:50:44 (UTC+8)
出版者:	國立中央大學
摘要:	我們在 Android 智慧型手機上開發了一套車牌辨識系統，這個系統的方法分為五個階段：影像前處理、車牌定位、字元切割、旋轉變形還原，及字元辨識；其主要特色有：(i) 可容忍車牌大小變化，遠近距離變化。(ii) 車牌定位不受車身顏色影響。(iii) 以車牌比例及車牌顏色篩選候選車牌區塊。(iv) 可容忍上下左右拍攝角度的變化。(v) 可辨識相似字元。在二值化處理後影像的車牌定位，我們以兩次水平掃描判斷明暗變化次數的方法執行，第一次先以最大車牌寬度為基準切割車牌範圍，第二次則以第一次找尋的結果加上車牌比例算出的寬度為基準切割車牌，所以對拍攝車牌的距離有相當的彈性。另外這種車牌定位是對車牌字元定位，而不是對車牌外框定位，所以可以不受車身顏色影響。在候選車牌區塊中，除了以車牌比例過濾外，另外再以車牌底色及字元顏色過濾，找出車牌區塊。在還原旋轉變形車牌方面，我們先以車牌文字的上下邊界之平均斜率還原車牌影像左右旋轉產生的變形；再以車牌種類修正字元區塊的總寬度。最後找出能使車牌影像高度最大且字元間隔距離最大及字元區塊數最多的角度，並以該角度還原車牌影像上下旋轉產生的變形。字元切割以垂直掃描方法進行；切割出個別區塊後，以車牌字元的長寬比例進一步過濾各個區塊；將不符合的區塊刪除，過濾後可以得到車牌影像區塊。我們的字元辨識是使用簡易的樣板比對方法；但是如果比對結果是數字 0、英文字母 O、D 和 Q；或是數字 8 和英文字母 B；或是數字 1 和英文字母 I 時，會再以字元特徵對這些字元進行第二次辨識，以減少這幾種相似字元誤判的機率。In this thesis, we develop a license plate recognition system on Android smart phone. The proposed method consists of five stages: image pre-processing, license plate locating, orientation correction, character segmentation, and character recognition. The major properties are: (i) Segmentation of license plates is invariant to size of license plates. (ii) Segmentation of license plates is invariant to color of cars. (iii) The license plate extraction method is based on length/width proportion and color of license plates. (iv) Segmentation of license plates tolerates to the orientation variation of license plates. (v) Similar characters are recognizable.After bi-level thresholding, we use horizontal scan twice to segment license plates. First, the width of a license plate is assumed to be the same as the width of whole image in the first scan. Second, base on the aspect ratio of the license plate, the height found by the first scan can be used to get the ideal width for the second scan. Hence segmentation of license plates is invariant to size of license plates. This segmentation is based on license-plate characters other than plates; thus, the segmentation is not influenced by body color of cars. In addition to using the aspect ratio of license plates to filter, we also use license-plate background and character color to extract license plates.There are three steps in resolving orientation distortion of license plates. First, using average slope of upper and lower bounds of the license plate text to correct the orientation from pan rotation. Second, using license plate types to amend the total width of character blocks. Then, to find out the rotation angle that makes the license-plate height is largest, the number of connected blocks of characters is largest. At last, we use this angle to correct the tilt rotation of license plate images.Character segmentation is implemented by vertical scan. If the aspect ratio is wrong, the corresponding segmented block will be deleted, and then each real character block of the license plate can be retrieved.Character recognition is done by template matching. If the detected character is digit 0, letter O, letter D, and letter Q, or digit 8 and letter B, or digit 1 and letter I, we further use special character features to recognize again. The second recognition process can reduce the wrong recognition rate of these similar characters.
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