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公开(公告)号：US09626556B2
公开(公告)日：2017-04-18
申请号：US14525152
申请日：2014-10-27
Applicant: GOOGLE INC.
Inventor： Xiaohang Wang , Alessandro Bissacco , Glenn Merlind Berntson , Marria Nazif , Justin Scheiner , Sam Shih , Mark Leslie Snyder , Daniel Talavera
CPC classification number: G06K9/00463 , G06K9/00979 , G06K9/036 , G06K9/18 , G06K9/4647 , G06K2209/01
Abstract: The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition (“OCR”). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.
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公开(公告)号：US20150139506A1
公开(公告)日：2015-05-21
申请号：US14525152
申请日：2014-10-27
Applicant: GOOGLE INC.
Inventor： Xiaohang Wang , Alessandro Bissacco , Glen Berntson , Marria Nazif , Justin Scheiner , Sam Shih , Mark Leslie Snyder , Daniel Talavera
CPC classification number: G06K9/00463 , G06K9/00979 , G06K9/036 , G06K9/18 , G06K9/4647 , G06K2209/01
Abstract: The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition (“OCR”). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.
Abstract translation: 本公开的技术包括计算机实现的方法，计算机程序产品和在发送到用于光学字符识别的系统（“OCR”）之前过滤图像的系统。 用户计算设备从物理卡的数字扫描中获取卡的第一图像并分析第一图像的特征，该分析足以确定第一图像是否可能被OCR算法可用。 如果用户计算设备确定第一图像可能是可用的，则将第一图像发送到与OCR算法相关联的OCR系统。 在确定第一图像不可能使用时，分析来自物理卡的数字扫描的卡的第二图像。 光学字符识别系统在滤波卡上执行光学字符识别算法。
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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：US09183452B2
公开(公告)日：2015-11-10
申请号：US14269777
申请日：2014-05-05
Applicant: Google Inc.
Inventor： Alessandro Bissacco , Hartmut Neven
CPC classification number: G06K9/34 , G06K9/32 , G06K9/3208 , G06K9/3233 , G06K9/342 , G06K9/6857 , G06K2209/01
Abstract: A text recognition server is configured to recognize text in a sparse text image. Specifically, given an image, the server specifies a plurality of “patches” (blocks of pixels within the image). The system applies a text detection algorithm to the patches to determine a number of the patches that contain text. This application of the text detection algorithm is used both to estimate the orientation of the image and to determine whether the image is textually sparse or textually dense. If the image is determined to be textually sparse, textual patches are identified and grouped into text regions, each of which is then separately processed by an OCR algorithm, and the recognized text for each region is combined into a result for the image as a whole.
Abstract translation: 文本识别服务器被配置为识别稀疏文本图像中的文本。 具体地，给定图像，服务器指定多个“补丁”（图像内的像素块）。 系统将文本检测算法应用于修补程序，以确定包含文本的多个修补程序。 文本检测算法的这种应用被用于估计图像的取向并确定图像是文本稀疏的还是文本密集的。 如果图像被确定为文本上稀疏的，则文本补丁被识别并分组成文本区域，然后每个文本区域被OCR算法分开处理，并且将每个区域的识别文本合并为整个图像的结果 。
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公开(公告)号：US09104914B1
公开(公告)日：2015-08-11
申请号：US14278450
申请日：2014-05-15
Applicant: Google Inc.
Inventor： Luc Vincent , Bo Wu , Ahmad Abdulkader , Marco Zennaro , Alessandro Bissacco , Hartwig Adam , Kong Man Cheung , Hartmut Neven , Andrea Lynn Frome
IPC: G06K9/00
CPC classification number: G06K9/00624 , G06K9/00228 , G06K9/00771 , G06K9/00791 , G06K9/72 , G06K2209/15
Abstract: Embodiments of this invention relate to detecting and blurring images. In an embodiment, a system detects objects in a photographic image. The system includes an object detector module configured to detect regions of the photographic image that include objects of a particular type at least based on the content of the photographic image. The system further includes a false positive detector module configured to determine whether each region detected by the object detector module includes an object of the particular type at least based on information about the context in which the photographic image was taken.
Abstract translation: 本发明的实施例涉及检测和模糊图像。 在一个实施例中，系统检测摄影图像中的对象。 该系统包括对象检测器模块，其被配置为至少基于摄影图像的内容来检测包括特定类型的对象的摄影图像的区域。 所述系统还包括假阳性检测器模块，其被配置为至少基于关于拍摄照片的上下文的信息来确定由对象检测器模块检测到的每个区域是否包括特定类型的对象。
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Patent Agency Ranking
公开(公告)号：US20150006360A1
公开(公告)日：2015-01-01
申请号：US14026738
申请日：2013-09-13
Applicant: GOOGLE INC.
Inventor： Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Yakov Okshtein , Farhan Shamsi , Alessandro Bissacco
IPC: G06Q20/32
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：US20150003733A1
公开(公告)日：2015-01-01
申请号：US14462711
申请日：2014-08-19
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: G06K9/32
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。
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公开(公告)号：US08903136B1
公开(公告)日：2014-12-02
申请号：US14133232
申请日：2013-12-18
Applicant: Google Inc.
Inventor： Xiaohang Wang , Alessandro Bissacco , Glen Berntson , Marria Nazif , Justin Scheiner , Sam Shih , Mark Leslie Snyder , Daniel Talavera
CPC classification number: G06K9/00463 , G06K9/00979 , G06K9/036 , G06K9/18 , G06K9/4647 , G06K2209/01
Abstract: The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition (“OCR”). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.
Abstract translation: 本公开的技术包括计算机实现的方法，计算机程序产品和在发送到用于光学字符识别的系统（“OCR”）之前过滤图像的系统。 用户计算设备从物理卡的数字扫描中获取卡的第一图像并分析第一图像的特征，该分析足以确定第一图像是否可能被OCR算法可用。 如果用户计算设备确定第一图像可能是可用的，则将第一图像发送到与OCR算法相关联的OCR系统。 在确定第一图像不可能使用时，分析来自物理卡的数字扫描的卡的第二图像。 光学字符识别系统在滤波卡上执行光学字符识别算法。
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公开(公告)号：US20160267345A1
公开(公告)日：2016-09-15
申请号：US15158520
申请日：2016-05-18
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.
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公开(公告)号：US20150006361A1
公开(公告)日：2015-01-01
申请号：US14026781
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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