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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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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：US10438131B1
公开(公告)日：2019-10-08
申请号：US14968293
申请日：2015-12-14
Applicant: GOOGLE INC.
Inventor： Jeffery Pennington , Sanjiv Kumar
Abstract: Implementations provide for use of spherical random features for polynomial kernels and large-scale learning. An example method includes receiving a polynomial kernel, approximating the polynomial kernel by generating a nonlinear randomized feature map, and storing the nonlinear feature map. Generating the nonlinear randomized feature map includes determining optimal coefficient values and standard deviation values for the polynomial kernel, determining an optimal probability distribution of vector values for the polynomial kernel based on a sum of Gaussian kernels that use the optimal coefficient values, selecting a sample of the vectors, and determining the nonlinear randomized feature map using the sampled vectors. Another example method includes normalizing a first feature vector for a data item, transforming the first feature vector into a second feature vector using a feature map that approximates a polynomial kernel with an explicit nonlinear feature map, and providing the second feature vector to a support vector machine.
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公开(公告)号：US09805290B2
公开(公告)日：2017-10-31
申请号：US14512893
申请日：2014-10-13
Applicant: GOOGLE INC.
Inventor： Krzysztof Marcin Choromanski , Sanjiv Kumar
IPC: G06K9/62
CPC classification number: G06K9/6255 , G06K9/622
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for clustering data points. One of the methods includes maintaining data representing a respective ordered tuple of skeleton data points for each of a plurality of clusters. One or more intersecting clusters are determined for a new data point. An updated tuple of skeleton data points is generated for an updated cluster by selecting updated skeleton data points, including selecting the new data point or an existing jth skeleton data point of one of the one or more intersecting clusters according to which random value, of the jth random value for the new data point or the random value for the jth existing skeleton data point, is closest to a limiting value. The new data point is then assigned to the updated cluster.
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Patent Agency Ranking
公开(公告)号：US09536160B2
公开(公告)日：2017-01-03
申请号：US14991516
申请日：2016-01-08
Applicant: GOOGLE INC.
Inventor： Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Jose Jeronimo Moreira Rodrigues
IPC: G06K9/00 , G06K9/18 , G06K9/62 , G06K9/66 , G06T3/00 , G06Q20/22 , G06Q20/34 , G07F7/08 , G06K9/32 , G06K9/46 , G06T7/00
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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47.发明授权公开(公告)号：US09514417B2
公开(公告)日：2016-12-06
申请号：US14143710
申请日：2013-12-30
Applicant: Google Inc.
Inventor： Sanjiv Kumar , Brian Kernighan
CPC classification number: G06N99/005 , G06F17/30011 , G06Q10/107
Abstract: Plagiarism may be detected, as disclosed herein, utilizing a database that stores documents for one or more courses. The database may restrict sharing of content between documents. A feature extraction module may receive edits and timestamp the edits to the document. A writing pattern for a particular user or group of users may be discerned from the temporal data and the documents for the particular user or group of users. A feature vector may be generated that represents the writing pattern. A machine learning technique may be applied to the feature vector to determine whether or not a document is plagiarized.
Abstract translation: 可以如本文所公开的那样利用存储用于一个或多个课程的文档的数据库来检测抄袭。 数据库可能限制文档之间的内容共享。 特征提取模块可以接收对文档的编辑和时间戳。 可以从特定用户或用户组的时间数据和文档中辨别特定用户或用户组的写入模式。 可以生成表示写入模式的特征向量。 可以将机器学习技术应用于特征向量以确定文档是否被剽窃。
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公开(公告)号：US09514359B2
公开(公告)日：2016-12-06
申请号：US13947020
申请日：2013-07-19
Applicant: GOOGLE INC.
Inventor： Marcus Quintana Mitchell , Peter Hazlehurst , Xiaohang Wang , Debra Lin Repenning , Yakov Okshtein , Sanjiv Kumar , Farhan Shamsi , Henry Allan Rowley
CPC classification number: G06K9/00449 , G06K9/00483 , G06K9/00536 , G06K2209/01
Abstract: Providing improved card art for display comprises receiving, by one or more computing devices, an image of a card and performing an image recognition algorithm on the image. The computing device identifies images represented on the card image and comparing the identified images to an image database. The computing device determines a standard card art image associated with the identified image based at least in part on the comparison and associates the standard card art image with an account of a user, the account being associated with the card in the image. The computing device displays the standard card art as a representation of the account.
Abstract translation: 提供用于显示的改进的卡片艺术品包括由一个或多个计算设备接收卡片的图像并在图像上执行图像识别算法。 计算设备识别卡片图像上表示的图像，并将识别的图像与图像数据库进行比较。 计算设备至少部分地基于比较来确定与所识别的图像相关联的标准卡片艺术图像，并且将标准卡片艺术图像与用户的帐户相关联，该帐户与图像中的卡片相关联。 计算设备将标准卡片艺术作为帐户的表示显示。
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公开(公告)号：US20160292527A1
公开(公告)日：2016-10-06
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
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：US20160063325A1
公开(公告)日：2016-03-03
申请号：US14934983
申请日：2015-11-06
Applicant: GOOGLE INC.
Inventor： Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Jose Jeronimo Moreira Rodrigues
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.
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