基于拍照的銀行卡卡號檢測
發(fā)布時間：2018-08-11 17:36
【摘要】：隨著成像設備的廣泛使用,只需要嵌入一個模塊,移動設備就可以通過拍照獲取的銀行卡圖像自動輸入銀行卡賬號而不用人工輸入。因此,基于拍照的銀行卡卡號檢測和識別技術具有重要的研究價值。和自然場景中的文本檢測一樣,基于拍照的銀行卡卡號檢測面臨著同樣的難題,卡號文本同樣存在字體、大小、排列方向上的多樣性,也受光照條件、透視變換和對比度的影響,另外,卡號的復雜背景也加重了卡號檢測和識別的難度。本文在自然場景中文本檢測的基礎上對基于拍照的銀行卡卡號檢測做了系統(tǒng)的研究,提出了基于特征提取、機器學習的卡號檢測方法。本文的主要工作如下:首先,本文算法是用來檢測水平卡號行的,需要水平校正銀行卡圖像。本文提出了兩種預處理算法來改進已經(jīng)提出了的Radon變換傾斜校正算法,第一種是對輸入圖像做邊緣檢測,第二種是對輸入圖像做直線段檢測,然后對邊緣或直線段圖像做Radon變換,檢測銀行卡的傾斜角度。實驗結果顯示兩種預處理改進能夠提高銀行卡圖像的傾斜校正效果。其次,根據(jù)卡號和它的相鄰背景間存在瞬態(tài)顏色,有一定的對比度,本文采用形態(tài)學算法來提取卡號的這種對比度特征;接下來,本文巧妙地將水平投影和k-means結合,得到了比較好的候選卡號行定位效果。最后,在卡號驗證過程中,本文對傳統(tǒng)的LBP算法進行了改進,提出了改進的LRBP(Region Local Binary Pattern)特征,該特征對卡號的紋理特征的描述能力更好,提高了銀行卡卡號行的檢測效果。接下來,算法分別提取了滑動窗的HOG和改進的LRBP特征通過訓練好的SVM分類器來驗證卡號域,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精度。最后通過實驗數(shù)據(jù)集檢測,本文算法能很好地檢測出銀行卡號。
[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.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
本文編號：2177734
[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.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
【參考文獻】
相關期刊論文 前10條
1 張華平;;常用判別分析方法的綜合比較[J];統(tǒng)計與決策;2015年22期
2 姜維;盧朝陽;李靜;劉曉佩;姚超;;基于視覺顯著性和提升框架的場景文字背景抑制方法[J];電子與信息學報;2014年03期
3 游佳;陳卉;;數(shù)字圖像中血管的分割與特征提取[J];生物醫(yī)學工程與臨床;2011年01期
4 袁海東;馬華東;黃曉冬;;基于梯度與粗糙度的視頻文本檢測與定位[J];電子學報;2008年08期
5 賈曉丹;李文舉;王海姣;;一種新的基于Radon變換的車牌傾斜校正方法[J];計算機工程與應用;2008年03期
6 潘梅森;郭國強;;基于圖像矩的車牌號碼傾斜校正[J];計算機輔助設計與圖形學學報;2007年08期
7 崔瑩瑩;楊杰;梁棟;;基于邊緣的標志牌文本提取方法[J];影像技術;2006年01期
8 包明,路小波;基于Hough變換的車牌傾斜檢測算法[J];交通與計算機;2004年02期
9 王良紅,王錦玲,梁延華;改進的Hough變換在校正汽車牌照傾斜中的應用[J];信息與電子工程;2004年01期
10 梁勇,李天牧;多方位形態(tài)學結構元素在圖像邊緣檢測中的應用[J];云南大學學報(自然科學版);1999年05期
相關會議論文 前1條
1 李鴻;彭宇新;肖建國;;一種視頻字幕檢測和識別的方法[A];全國網(wǎng)絡與信息安全技術研討會論文集（下冊）[C];2007年
相關碩士學位論文 前1條
1 張麗;基于小波的視頻中人工文本檢測方法研究[D];哈爾濱工程大學;2007年
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