【摘要】：隨著成像設(shè)備的廣泛使用,只需要嵌入一個(gè)模塊,移動(dòng)設(shè)備就可以通過拍照獲取的銀行卡圖像自動(dòng)輸入銀行卡賬號(hào)而不用人工輸入。因此,基于拍照的銀行卡卡號(hào)檢測(cè)和識(shí)別技術(shù)具有重要的研究?jī)r(jià)值。和自然場(chǎng)景中的文本檢測(cè)一樣,基于拍照的銀行卡卡號(hào)檢測(cè)面臨著同樣的難題,卡號(hào)文本同樣存在字體、大小、排列方向上的多樣性,也受光照條件、透視變換和對(duì)比度的影響,另外,卡號(hào)的復(fù)雜背景也加重了卡號(hào)檢測(cè)和識(shí)別的難度。本文在自然場(chǎng)景中文本檢測(cè)的基礎(chǔ)上對(duì)基于拍照的銀行卡卡號(hào)檢測(cè)做了系統(tǒng)的研究,提出了基于特征提取、機(jī)器學(xué)習(xí)的卡號(hào)檢測(cè)方法。本文的主要工作如下:首先,本文算法是用來檢測(cè)水平卡號(hào)行的,需要水平校正銀行卡圖像。本文提出了兩種預(yù)處理算法來改進(jìn)已經(jīng)提出了的Radon變換傾斜校正算法,第一種是對(duì)輸入圖像做邊緣檢測(cè),第二種是對(duì)輸入圖像做直線段檢測(cè),然后對(duì)邊緣或直線段圖像做Radon變換,檢測(cè)銀行卡的傾斜角度。實(shí)驗(yàn)結(jié)果顯示兩種預(yù)處理改進(jìn)能夠提高銀行卡圖像的傾斜校正效果。其次,根據(jù)卡號(hào)和它的相鄰背景間存在瞬態(tài)顏色,有一定的對(duì)比度,本文采用形態(tài)學(xué)算法來提取卡號(hào)的這種對(duì)比度特征;接下來,本文巧妙地將水平投影和k-means結(jié)合,得到了比較好的候選卡號(hào)行定位效果。最后,在卡號(hào)驗(yàn)證過程中,本文對(duì)傳統(tǒng)的LBP算法進(jìn)行了改進(jìn),提出了改進(jìn)的LRBP(Region Local Binary Pattern)特征,該特征對(duì)卡號(hào)的紋理特征的描述能力更好,提高了銀行卡卡號(hào)行的檢測(cè)效果。接下來,算法分別提取了滑動(dòng)窗的HOG和改進(jìn)的LRBP特征通過訓(xùn)練好的SVM分類器來驗(yàn)證卡號(hào)域,在這一過程中,算法使用了分類器集成來提高分類器的檢測(cè)精度。最后通過實(shí)驗(yàn)數(shù)據(jù)集檢測(cè),本文算法能很好地檢測(cè)出銀行卡號(hào)。
[Abstract]:With the wide use of imaging equipment, only one module needs to be embedded, and the mobile device can automatically input the bank card account without manual input by taking pictures of the bank card image. Therefore, the bank card number detection and recognition technology based on photograph has important research value. Like text detection in natural scenes, the bank card number detection based on taking pictures faces the same problem. The card number text also has the diversity of font, size, arrangement direction, and is also subject to illumination conditions. The influence of perspective transformation and contrast, in addition, the complex background of card number also increases the difficulty of card number detection and recognition. Based on the Chinese text detection of natural scene, this paper makes a systematic research on the bank card number detection based on taking pictures, and puts forward a method of card number detection based on feature extraction and machine learning. The main work of this paper is as follows: firstly, the algorithm is used to detect the horizontal card number line, and the horizontal correction of bank card image is needed. In this paper, two preprocessing algorithms are proposed to improve the proposed Radon transform skew correction algorithm. The first is to detect the edge of the input image, and the second is to detect the line segment of the input image. Then the edge or straight line image is transformed by Radon to detect the tilt angle of bank card. The experimental results show that the two preprocessing improvements can improve the skew correction effect of bank card image. Secondly, according to the transient color between the card number and its adjacent background, there is a certain contrast. In this paper, morphological algorithm is used to extract the contrast feature of the card number. Then, the horizontal projection and k-means are skillfully combined in this paper. A good candidate card number line location effect is obtained. Finally, in the process of card number verification, the traditional LBP algorithm is improved, and an improved LRBP (Region Local Binary Pattern) feature is proposed, which can describe the texture feature of the card number better and improve the detection effect of the bank card number line. Then, the HOG of sliding window and the improved LRBP feature are extracted respectively to verify the card number domain through the trained SVM classifier. In this process, the classifier integration is used to improve the detection accuracy of the classifier. Finally, through the experimental data set detection, the algorithm can detect the bank card number well.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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