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    COMPARING EXTRACTED CARD DATA WITH USER DATA
    22.
    发明申请
    COMPARING EXTRACTED CARD DATA WITH USER DATA 有权
    使用用户数据比较提取的卡数据

    公开(公告)号：US20160132740A1

    公开(公告)日：2016-05-12

    申请号：US14504214

    申请日：2014-10-01

    Applicant: GOOGLE INC.

    Inventor： Xiaohang Wang ,  Farhan Shamsi ,  Sanjiv Kumar ,  Henry Allan Rowley ,  Marcus Quintana Mitchell

    IPC: G06K9/18 ,  G06K9/62

    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。

    Annotating images
    23.
    发明授权
    Annotating images 有权

    公开(公告)号：US09298682B2

    公开(公告)日：2016-03-29

    申请号：US13799307

    申请日：2013-03-13

    Applicant: Google Inc.

    Inventor： Ameesh Makadia ,  Sanjiv Kumar

    IPC: G06K9/54 ,  G06F17/24 ,  G06F17/30 ,  G06K9/00 ,  G06K9/46

    CPC classification number: G06F17/241 ,  G06F17/30265 ,  G06K9/00664 ,  G06K9/46

    Abstract: Methods, systems, and apparatus, including computer program products, for generating data for annotating images automatically. In one aspect, a method includes receiving an input image, identifying one or more nearest neighbor images of the input image from among a collection of images, in which each of the one or more nearest neighbor images is associated with a respective one or more image labels, assigning a plurality of image labels to the input image, in which the plurality of image labels are selected from the image labels associated with the one or more nearest neighbor images, and storing in a data repository the input image having the assigned plurality of image labels. In another aspect, a method includes assigning a single image label to the input image, in which the single image label is selected from labels associated with multiple ranked nearest neighbor images.

    CACHING SPEECH RECOGNITION SCORES
    25.
    发明申请
    CACHING SPEECH RECOGNITION SCORES 有权
    缓存语音识别码

    公开(公告)号：US20150371631A1

    公开(公告)日：2015-12-24

    申请号：US14311557

    申请日：2014-06-23

    Applicant: Google Inc.

    Inventor： Eugene Weinstein ,  Sanjiv Kumar ,  Ignacio L. Moreno ,  Andrew W. Senior ,  Nikhil Prasad Bhat

    IPC: G10L15/14 ,  G10L19/038

    CPC classification number: G10L15/08 ,  G10L15/285

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for caching speech recognition scores. In some implementations, one or more values comprising data about an utterance are received. An index value is determined for the one or more values. An acoustic model score for the one or more received values is selected, from a cache of acoustic model scores that were computed before receiving the one or more values, based on the index value. A transcription for the utterance is determined using the selected acoustic model score.

    Abstract translation: 方法，系统和装置，包括编码在计算机存储介质上的用于缓存语音识别分数的计算机程序。 在一些实现中，接收包括关于话语的数据的一个或多个值。 确定一个或多个值的索引值。 基于索引值，从接收到一个或多个值之前计算的声学模型分数的高速缓存中选择一个或多个接收值的声学模型分数。 使用所选择的声学模型得分确定发音的转录。

    Extracting card data from multiple cards
    26.
    发明授权
    Extracting card data from multiple cards 有权
    从多张卡中提取卡片数据

    公开(公告)号：US09152858B2

    公开(公告)日：2015-10-06

    申请号：US13931966

    申请日：2013-06-30

    Applicant: GOOGLE INC.

    Inventor： Xiaohang Wang ,  Jeff Huber ,  Farhan Shamsi ,  Yakov Okshtein ,  Sanjiv Kumar ,  Henry Allan Rowley ,  Marcus Quintana Mitchell ,  Debra Lin Repenning

    IPC: G06K9/00 ,  G06K9/32

    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。

    HIERARCHICAL CLASSIFICATION IN CREDIT CARD DATA EXTRACTION
    28.
    发明申请
    HIERARCHICAL CLASSIFICATION IN CREDIT CARD DATA EXTRACTION 有权
    信用卡数据提取中的分层分类

    公开(公告)号：US20150003748A1

    公开(公告)日：2015-01-01

    申请号：US14059071

    申请日：2013-10-21

    Applicant: GOOGLE INC.

    Inventor： Sanjiv Kumar ,  Henry Allan Rowley ,  Xiaohang Wang ,  Jose Jeronimo Moreira Rodrigues

    IPC: G06K9/00

    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。

    EXTRACTING CARD DATA WITH WEAR PATTERNS
    29.
    发明申请
    EXTRACTING CARD DATA WITH WEAR PATTERNS 有权
    提取卡片数据与磨损模式

    公开(公告)号：US20150003667A1

    公开(公告)日：2015-01-01

    申请号：US14091093

    申请日：2013-11-26

    Applicant: GOOGLE INC.

    Inventor： Henry Allan Rowley ,  Sanjiv Kumar ,  Xiaohang Wang ,  Alessandro Bissacco ,  Jose Jeronimo Moreira Rodrigues ,  Kishore Ananda Papineni

    IPC: G06T3/00 ,  G06K9/62 ,  G06K9/18 ,  G06K9/66

    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应用程序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。

    Hierarchical quantization for fast inner product search
    30.
    发明授权
    Hierarchical quantization for fast inner product search 有权

    公开(公告)号：US10719509B2

    公开(公告)日：2020-07-21

    申请号：US15290198

    申请日：2016-10-11

    Applicant: GOOGLE INC.

    Inventor： Sanjiv Kumar ,  David Morris Simcha ,  Ananda Theertha Suresh ,  Ruiqi Guo ,  Xinnan Yu ,  Daniel Holtmann-Rice

    IPC: G06F16/2453 ,  G06F16/28 ,  G06F16/22 ,  G06F16/2457 ,  G06F17/10 ,  G06K9/62 ,  G06F16/33 ,  G06F16/35

    Abstract: Implementations provide an efficient system for calculating inner products between high-dimensionality vectors. An example method includes clustering database items represented as vectors, selecting a cluster center for each cluster, and storing the cluster center as an entry in a first layer codebook. The method also includes, for each database item, calculating a residual based on the cluster center for the cluster the database item is assigned to and projecting the residual into subspaces. The method also includes determining, for each of the subspaces, an entry in a second layer codebook for the subspace, and storing the entry in the first layer codebook and the respective entry in the second layer codebook for each of the subspaces as a quantized vector for the database item. The entry can be used to categorize an item represented by a query vector or to provide database items responsive to a query vector.

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