公开(公告)号：US20150169644A1

公开(公告)日：2015-06-18

申请号：US13733335

申请日：2013-01-03

Applicant: Google Inc.

Inventor： Yunchao Gong ,  Sanjiv Kumar ,  Henry Allan Rowley

IPC: G06F17/30

CPC classification number: G06F16/532

Abstract: Separately optimizing angle error and magnitude error of a search query entered into a query database may be referred to as the “shape-gain” separation quantization. Each of a direction and a magnitude for each of a plurality of database vectors may be separately encoded. A query vector may be received. The query vector may include a query direction and a query magnitude. The separately encoded query direction, query magnitude, and each of the separately encoded direction and magnitude for each of the plurality of database vectors may be combined. Distances between the query vector and each of the plurality of database vectors may be determined. At least one of the plurality of database vectors that is similar to the query vector may be identified based on the determined distances.

Abstract translation: 单独优化输入查询数据库的搜索查询的角度误差和幅度误差可以称为“形状增益”分离量化。 可以分别编码多个数据库向量中的每一个的方向和幅度的每一个。 可以接收查询向量。 查询向量可以包括查询方向和查询量级。 可以组合用于多个数据库向量中的每一个的单独编码的查询方向，查询量级以及单独编码的方向和幅度中的每一个。 可以确定查询向量与多个数据库向量中的每一个之间的距离。 可以基于所确定的距离来识别类似于查询向量的多个数据库向量中的至少一个。

公开(公告)号：US20150169635A1

公开(公告)日：2015-06-18

申请号：US14492515

申请日：2014-09-22

Applicant: Google Inc.

Inventor： Yushi Jing ,  Henry Allan Rowley ,  Aparna Chennapragada

IPC: G06F17/30

CPC classification number: G06F17/30247 ,  G06F17/30244 ,  G06F17/30265 ,  G06F17/30274 ,  G06F17/3028 ,  G06F17/30554 ,  G06F17/30598 ,  G06F17/30867

Abstract: This specification relates to presenting image search results. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving an image query, the image query being a query for image search results; receiving ranked image search results responsive to the image query, the image search results each including an identification of a corresponding image resource; generating a similarity matrix for images identified by the image search results; generating a hierarchical grouping of the images using the similarity matrix; identifying a canonical image for each group in the hierarchical grouping using a ranking measure; and presenting a visual representation of the image search results based on the hierarchical grouping and the identified canonical images.

Abstract translation: 本说明书涉及呈现图像搜索结果。 通常，本说明书中描述的主题的一个方面可以体现在包括接收图像查询的动作，图像查询是图像搜索结果的查询的方法中; 响应于图像查询接收排序图像搜索结果，图像搜索结果各自包括相应图像资源的标识; 生成由图像搜索结果识别的图像的相似性矩阵; 使用相似性矩阵生成图像的分层分组; 使用排序度量来识别分层分组中的每个组的规范图像; 并且基于分层分组和识别的规范图像来呈现图像搜索结果的视觉表示。

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。

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。

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。

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

IPC: G06K9/18 ,  G06K9/32

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。

公开(公告)号：US20140258263A1

公开(公告)日：2014-09-11

申请号：US14281994

申请日：2014-05-20

Applicant: Google Inc.

Inventor： Shumeet Baluja ,  Henry Allan Rowley

IPC: G06F17/30

CPC classification number: G06F17/30554 ,  G06F3/0482 ,  G06F3/04842 ,  G06F17/30864 ,  G06F17/30867 ,  G06F17/30882

Abstract: A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

Abstract translation: 用于生成客户端请求的查询的结果的计算机实现的方法涉及接收由客户端通信设备产生的查询，响应于接收查询而产生用于查询的结果，在接收之前确定一个或多个预测性后续请求 来自客户端设备的实际后续请求，以及启动与所述一个或多个预测性后续请求相关联的信息的检索，以及将所述结果的至少一部分发送到所述客户端设备，然后至少向所述客户端设备发送 与一个或多个预测性跟踪请求相关联的信息的一部分。

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

IPC: G06K9/03 ,  G06K9/20

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。

公开(公告)号：US09830367B2

公开(公告)日：2017-11-28

申请号：US15044568

申请日：2016-02-16

Applicant: Google Inc.

Inventor： Shumeet Baluja ,  Henry Allan Rowley

IPC: G06F17/30 ,  G06F3/0482 ,  G06F3/0484

CPC classification number: G06F17/30554 ,  G06F3/0482 ,  G06F3/04842 ,  G06F17/30864 ,  G06F17/30867 ,  G06F17/30882

Abstract: A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

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。