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公开(公告)号:US20150371086A1
公开(公告)日:2015-12-24
申请号:US14837605
申请日:2015-08-27
Applicant: GOOGLE INC.
Inventor: Xiaohang Wang , Jeff Huber , Farhan Shamsi , Yakov Okshtein , Sanjiv Kumar , Henry Allan Rowley , Marcus Quintana Mitchell , Debra Lin Repenning
CPC classification number: G06K9/00469 , G06K9/00463 , G06K9/2063 , G06K9/3283 , G06K9/6201 , G06K2209/01
Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.
Abstract translation: 以轻松对准的方式提取金融卡信息包括接收卡的图像的方法,在图像的位置确定一个或多个边缘查找器区域,并识别一个或多个边缘查找器区域中的线。 该方法还识别由所识别的线的外插的交点形成的一个或多个四边形,确定一个或多个四边形的纵横比,并将确定的四边形的纵横比与预期的纵横比进行比较。 然后,该方法识别与预期宽高比匹配的四边形,并在整流模型上执行光学字符识别算法。 在图像中的多个卡上执行类似的方法。 比较每个卡的分析结果,提高数据的准确性。
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公开(公告)号:US20150086069A1
公开(公告)日:2015-03-26
申请号:US14559888
申请日:2014-12-03
Applicant: GOOGLE INC.
Inventor: Sanjiv Kumar , Xiaohang Wang , Jose Moreira Rodrigues , Farhan Shamsi , Yakov Okshtein , Henry Allan Rowley , Marcus Quintana Mitchell , Zhifei Li
IPC: G06K9/00
CPC classification number: G06K9/186 , G06K7/10 , G06K9/00469 , G06K9/18 , G06K9/2054 , G06K9/228 , G06K9/6202 , G06K2209/01 , G06Q20/32 , G06Q20/3223 , G06Q20/3276 , G06Q20/34 , G06Q20/36 , H04N1/00307
Abstract: Extracting card data comprises receiving, by one or more computing devices, a digital image of a card; perform an image recognition process on the digital representation of the card; identifying an image in the digital representation of the card; comparing the identified image to an image database comprising a plurality of images and determining that the identified image matches a stored image in the image database; determining a card type associated with the stored image and associating the card type with the card based on the determination that the identified image matches the stored image; and performing a particular optical character recognition algorithm on the digital representation of the card, the particular optical character recognition algorithm being based on the determined card type. Another example uses an issuer identification number to improve data extraction. Another example compares extracted data with user data to improve accuracy.
Abstract translation: 提取卡数据包括由一个或多个计算设备接收卡的数字图像; 对卡的数字表示进行图像识别处理; 识别卡的数字表示中的图像; 将所识别的图像与包括多个图像的图像数据库进行比较,并确定所识别的图像与图像数据库中存储的图像匹配; 基于所识别的图像与所存储的图像匹配的确定来确定与所存储的图像相关联的卡类型并将卡类型与卡相关联; 以及对所述卡的数字表示执行特定光学字符识别算法,所述特定光学字符识别算法基于所确定的卡类型。 另一个例子是使用发行人识别号来改进数据提取。 另一个例子比较了提取的数据与用户数据,以提高准确性。
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公开(公告)号:US20150006362A1
公开(公告)日:2015-01-01
申请号:US14062655
申请日:2013-10-24
Applicant: GOOGLE INC.
Inventor: Marcus Quintana Mitchell , Xiaohang Wang , Farhan Shamsi , Yakov Okshtein , Sanjiv Kumar , Henry Allan Rowley , Debra Lin Repenning
IPC: G06Q20/32
CPC classification number: G06K9/186 , G06K7/10 , G06K9/00469 , G06K9/03 , G06K9/18 , G06K9/2054 , G06K9/228 , G06K9/6202 , G06K2209/01 , G06Q20/32 , G06Q20/3223 , G06Q20/3276 , G06Q20/34 , G06Q20/36 , H04N1/00307
Abstract: Extracting card data comprises receiving, by one or more computing devices, a digital image of a card; perform an image recognition process on the digital representation of the card; identifying an image in the digital representation of the card; comparing the identified image to an image database comprising a plurality of images and determining that the identified image matches a stored image in the image database; determining a card type associated with the stored image and associating the card type with the card based on the determination that the identified image matches the stored image; and performing a particular optical character recognition algorithm on the digital representation of the card, the particular optical character recognition algorithm being based on the determined card type. Another example uses an issuer identification number to improve data extraction. Another example compares extracted data with user data to improve accuracy.
Abstract translation: 提取卡数据包括由一个或多个计算设备接收卡的数字图像; 对卡的数字表示进行图像识别处理; 识别卡的数字表示中的图像; 将所识别的图像与包括多个图像的图像数据库进行比较,并确定所识别的图像与图像数据库中存储的图像匹配; 基于所识别的图像与所存储的图像匹配的确定来确定与所存储的图像相关联的卡类型并将卡类型与卡相关联; 以及对所述卡的数字表示执行特定光学字符识别算法,所述特定光学字符识别算法基于所确定的卡类型。 另一个例子是使用发行人识别号来改进数据提取。 另一个例子比较了提取的数据与用户数据,以提高准确性。
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公开(公告)号:US20150003719A1
公开(公告)日:2015-01-01
申请号:US14461001
申请日:2014-08-15
Applicant: GOOGLE INC.
Inventor: Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Jose Jeronimo Moreira Rodrigues
IPC: G06K9/18
CPC classification number: G06K9/6269 , G06K9/00469 , G06K9/00536 , G06K9/18 , G06K9/186 , G06K9/3233 , G06K9/3258 , G06K9/46 , G06K9/6202 , G06K9/6267 , G06K9/66 , G06K2009/4666 , G06K2209/01 , G06Q20/227 , G06Q20/34 , G06T3/0012 , G06T7/11 , G06T2207/20132 , G07F7/0893
Abstract: Embodiments herein provide computer-implemented techniques for allowing a user computing device to extract financial card information using optical character recognition (“OCR”). Extracting financial card information may be improved by applying various classifiers and other transformations to the image data. For example, applying a linear classifier to the image to determine digit locations before applying the OCR algorithm allows the user computing device to use less processing capacity to extract accurate card data. The OCR application may train a classifier to use the wear patterns of a card to improve OCR algorithm performance. The OCR application may apply a linear classifier and then a nonlinear classifier to improve the performance and the accuracy of the OCR algorithm. The OCR application uses the known digit patterns used by typical credit and debit cards to improve the accuracy of the OCR algorithm.
Abstract translation: 这里的实施例提供了计算机实现的技术,用于允许用户计算设备使用光学字符识别(“OCR”)提取金融卡信息。 可以通过对图像数据应用各种分类器和其他变换来提高金融卡信息的提取。 例如,在应用OCR算法之前,对图像应用线性分类器以确定数字位置允许用户计算设备使用较少的处理能力来提取准确的卡数据。 OCR应用程序可以训练分类器来使用卡的磨损模式来改善OCR算法性能。 OCR应用可以应用线性分类器,然后应用非线性分类器来提高OCR算法的性能和准确性。 OCR应用程序使用典型的信用卡和借记卡使用的已知数字模式来提高OCR算法的准确性。
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公开(公告)号:US08837833B1
公开(公告)日:2014-09-16
申请号:US14104901
申请日:2013-12-12
Applicant: Google Inc.
Inventor: Xiaohang Wang , Farhan Shamsi , Yakov Okshtein , Sanjiv Kumar , Henry Allan Rowley , Marcus Quintana Mitchell , Debra Lin Repenning , Alessandro Bissacco , Justin Scheiner , Leon Palm
CPC classification number: G06K9/3283 , G06K9/00469 , G06K9/18 , G06K9/228 , G06K9/325 , G06K9/40 , G06K2009/363 , G06K2209/01
Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.
Abstract translation: 以轻松对准的方式提取金融卡信息包括接收卡的图像的方法,在图像的位置确定一个或多个边缘查找器区域,并识别一个或多个边缘查找器区域中的线。 该方法还识别由所识别的线的外插的交点形成的一个或多个四边形,确定一个或多个四边形的纵横比,并将确定的四边形的纵横比与预期的纵横比进行比较。 然后,该方法识别与预期宽高比匹配的四边形,并在整流模型上执行光学字符识别算法。 在图像中的多个卡上执行类似的方法。 比较每个卡的分析结果,提高数据的准确性。
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46.
公开(公告)号:US08805125B1
公开(公告)日:2014-08-12
申请号:US14026479
申请日:2013-09-13
Applicant: Google Inc.
Inventor: Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Yakov Okshtein , Farhan Shamsi , Alessandro Bissacco
CPC classification number: G06K9/6201 , G06K9/00201 , G06K9/00469 , G06K9/00483 , G06K9/03 , G06K9/036 , G06K9/18 , G06K9/20 , G06K9/2054 , G06K9/228 , G06K9/344 , G06K9/6202 , G06K9/78 , G06K2009/2045 , G06K2209/01 , G06K2209/40 , G06Q20/322 , G06Q20/327 , G06Q20/34 , G06Q20/4016 , G06T17/00
Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.
Abstract translation: 比较来自连续扫描的提取的卡数据包括由一个或多个计算设备接收卡的数字扫描; 从所述物理卡的数字扫描中获取所述卡的多个图像; 对所述多个图像中的每一个执行光学字符识别算法; 比较针对所述多个图像中的每一个的所述光学字符识别算法的应用结果; 确定所述多个图像中的每一个的结果的配置阈值是否彼此匹配; 以及当多个图像中的每一个的结果彼此匹配时验证结果。 可以采用提取的卡数据的阈值置信水平来确定提取的准确性。 从混合图像和卡片的三维模型进一步提取数据。 图像中的压纹文字和全息图可能被用来防止欺诈。
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公开(公告)号:US10152647B2
公开(公告)日:2018-12-11
申请号:US15184198
申请日:2016-06-16
Applicant: GOOGLE INC.
Inventor: Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Yakov Okshtein , Farhan Shamsi , Alessandro Bissacco
IPC: G06K9/18 , G06K9/62 , G06Q20/34 , G06Q20/40 , G06Q20/32 , G06K9/78 , G06K9/00 , G06K9/03 , G06K9/20 , G06K9/22 , G06K9/34 , G06T17/00
Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.
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48.
公开(公告)号:US09904956B2
公开(公告)日:2018-02-27
申请号:US14551991
申请日:2014-11-24
Applicant: GOOGLE INC.
Inventor: Xiaohang Wang , Glenn Berntson
CPC classification number: G06Q40/00 , G06K9/00442 , G06K9/18 , G06K9/3258 , G06K2209/01 , G06Q20/34 , G06Q20/351 , G06Q20/36 , H04N5/2257
Abstract: A user captures an image of a payment card via a user computing device camera. An optical character recognition system receives the payment card image from the user computing device. The system performs optical character recognition and visual object recognition algorithms on the payment card image to extract text and visual objects from the payment card image, which are used by the system to identify a payment card type. The system may categorize the payment card as a credit card or a non-credit card. In an example embodiment, the system determines that the payment card type is a credit card and transmits fee structure to the user. The user selects a second payment card for use in the transaction and the transaction is processed using financial account information associated with the second payment card.
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公开(公告)号:US09892317B2
公开(公告)日:2018-02-13
申请号:US15297127
申请日:2016-10-18
Applicant: GOOGLE INC.
Inventor: Xiaohang Wang , Jeff Huber , Farhan Shamsi , Yakov Okshtein , Sanjiv Kumar , Henry Allan Rowley , Marcus Quintana Mitchell , Debra Lin Repenning
CPC classification number: G06K9/00469 , G06K9/00463 , G06K9/2063 , G06K9/3283 , G06K9/6201 , G06K2209/01
Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.
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公开(公告)号:US20180039857A1
公开(公告)日:2018-02-08
申请号:US15655849
申请日:2017-07-20
Applicant: GOOGLE INC.
Inventor: Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Yakov Okshtein , Farhan Shamsi , Alessandro Bissacco
IPC: G06K9/62
CPC classification number: G06K9/6201 , G06K9/00201 , G06K9/00469 , G06K9/00483 , G06K9/03 , G06K9/036 , G06K9/18 , G06K9/20 , G06K9/2054 , G06K9/228 , G06K9/344 , G06K9/6202 , G06K9/78 , G06K2009/2045 , G06K2209/01 , G06K2209/40 , G06Q20/322 , G06Q20/327 , G06Q20/34 , G06Q20/4016 , G06T17/00
Abstract: Comparing extracted card data from a continuous scan comprises an optical character recognition (“OCR”) system for extracted data based on three-dimensional models. The system receives a digital scan of a physical card and obtains a plurality of images of the card from the digital scan of the physical card. The system performs an OCR algorithm on a three-dimensional model based on the images and determines if a confidence level of the results are above a preconfigured level. If the results are below the preconfigured levels, a second three dimensional model is created that includes additional received images. When results are over the preconfigured level, the results are accepted as an accurate extraction.
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