Content-based image ranking
    3.
    发明授权
    Content-based image ranking 有权
    基于内容的图像排名

    公开(公告)号:US09436707B2

    公开(公告)日:2016-09-06

    申请号:US14330195

    申请日:2014-07-14

    Applicant: Google Inc.

    Abstract: Methods, systems, and apparatus, including computer program products, for ranking search results for queries. The method includes calculating a visual similarity score for one or more pairs of images in a plurality of images based on visual features of images in each of the one or more pairs; building a graph of images by linking each of one or more images in the plurality of images to one or more nearest neighbor images based on the visual similarity scores; associating a respective score with each of one or more images in the graph based on data indicative of user behavior relative to the image as a search result for a query; and determining a new score for each of one or more images in the graph based on the respective score of the image, and the respective scores of one or more nearest neighbors to the image.

    Abstract translation: 方法,系统和装置,包括计算机程序产品,用于对查询的搜索结果进行排名。 该方法包括基于一个或多个对中的每一个中的图像的视觉特征来计算多个图像中的一对或多对图像的视觉相似性分数; 通过基于所述视觉相似性得分将所述多个图像中的一个或多个图像的每一个链接到一个或多个最近邻图像来构建图像的图; 基于表示用户相对于图像的行为的数据作为查询的搜索结果,将各个分数与图中的一个或多个图像中的每一个相关联; 以及基于所述图像的相应分数以及所述图像的一个或多个最近邻居的各个分数来确定所述图中的一个或多个图像中的每一个的新分数。

    Segmentation of an input by cut point classification
    5.
    发明授权
    Segmentation of an input by cut point classification 有权
    通过切点分类对输入进行分割

    公开(公告)号:US09286527B2

    公开(公告)日:2016-03-15

    申请号:US14184997

    申请日:2014-02-20

    Applicant: Google Inc.

    Abstract: Techniques are provided for segmenting an input by cut point classification and training a cut classifier. A method may include receiving, by a computerized text recognition system, an input in a script. A heuristic may be applied to the input to insert multiple cut points. For each of the cut points, a probability may be generated and the probability may indicate a likelihood that the cut point is correct. Multiple segments of the input may be selected, and the segments may be defined by cut points having a probability over a threshold. Next, the segments of the input may be provided to a character recognizer. Additionally, a method may include training a cut classifier using a machine learning technique, based on multiple text training examples, to determine the correctness of a cut point in an input.

    Abstract translation: 提供了通过切点分类对输入进行分割和训练切分分类器的技术。 方法可以包括通过计算机化的文本识别系统接收脚本中的输入。 可以将启发式应用于输入以插入多个切割点。 对于每个切割点,可以产生概率,并且概率可以指示切割点是正确的可能性。 可以选择输入的多个段,并且可以通过具有超过阈值的概率的切点来定义段。 接下来,可以将输入的段提供给字符识别器。 另外,一种方法可以包括基于多个文本训练示例使用机器学习技术来训练切割分类器,以确定输入中的切割点的正确性。

    EXTRACTION OF FINANCIAL ACCOUNT INFORMATION FROM A DIGITAL IMAGE OF A CARD
    6.
    发明申请
    EXTRACTION OF FINANCIAL ACCOUNT INFORMATION FROM A DIGITAL IMAGE OF A CARD 审中-公开
    从卡的数字图像中提取财务帐户信息

    公开(公告)号:US20150287002A1

    公开(公告)日:2015-10-08

    申请号:US14743455

    申请日:2015-06-18

    Applicant: GOOGLE INC.

    Abstract: Capturing information from payment instruments comprises receiving, using one or more computer devices, an image of a back side of a payment instrument, the payment instrument comprising information imprinted thereon such that the imprinted information protrudes from a front side of the payment instrument and the imprinted information is indented into the back side of the payment instrument; extracting sets of characters from the image of the back side of the payment instrument based on the imprinted information indented into the back side of the payment instrument and depicted in the image of the back side of the payment instrument; applying a first character recognition application to process the sets of characters extracted from the image of the back side of the payment instrument; and categorizing each of the sets of characters into one of a plurality of categories relating to information required to conduct a payment transaction.

    Abstract translation: 从支付工具获取信息包括:使用一个或多个计算机设备接收支付工具的背面的图像,所述支付工具包括印在其上的信息,使得所述打印信息从所述支付工具的正面突出并且所述印刷 信息缩进到支付工具的背面; 基于所述支付工具背面的印刷信息,从所述支付工具的背面图像中提取出的字符集,并以所述支付工具的背面的图像形式示出; 应用第一字符识别应用来处理从所述支付工具的背面的图像提取的字符集; 以及将每个所述字符集分类为与执行支付交易所需的信息相关的多个类别中的一个。

    Grouping of image search results
    7.
    发明授权
    Grouping of image search results 有权
    分组图像搜索结果

    公开(公告)号:US09116921B2

    公开(公告)日:2015-08-25

    申请号:US14492515

    申请日:2014-09-22

    Applicant: Google Inc.

    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。

    Segmentation of Devanagari-Script Handwriting for Recognition
    9.
    发明申请
    Segmentation of Devanagari-Script Handwriting for Recognition 有权
    梵文脚本手写识别的分割

    公开(公告)号:US20150169949A1

    公开(公告)日:2015-06-18

    申请号:US14106893

    申请日:2013-12-16

    Applicant: GOOGLE INC.

    CPC classification number: G06K9/00416 G06K9/344 G06K2209/013

    Abstract: Methods and systems for recognizing Devanagari script handwriting are provided. A method may include receiving a handwritten input and determining that the handwritten input comprises a shirorekha stroke based on one or more shirorekha detection criteria. Shirorekha detection criteria may be at least one criterion such as a length of the shirorekha stroke, a horizontality of the shirorekha stroke, a straightness of the shirorekha stroke, a position in time at which the shirorekha stroke is made in relation to one or more other strokes in the handwritten input, and the like. Next, one or more recognized characters may be provided corresponding to the handwritten input.

    Abstract translation: 提供了识别梵文脚本手写的方法和系统。 方法可以包括接收手写输入并且基于一个或多个shirorekha检测标准确定手写输入包括shirorekha笔划。 Shirorekha检测标准可以是至少一个标准,例如shirorekha中风的长度,shirorekha中风的水平度,shirorekha中风的平直度,相对于一个或多个其他的shirorekha中风的时间位置 手写输入中的笔画等。 接下来,可以对应于手写输入提供一个或多个识别的字符。

    Extracting Card Data Using Three-Dimensional Models
    10.
    发明申请
    Extracting Card Data Using Three-Dimensional Models 审中-公开
    使用三维模型提取卡片数据

    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.

    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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