-
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。
-
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。
-
公开(公告)号：US20160098595A1
公开(公告)日：2016-04-07
申请号：US14967901
申请日：2015-12-14
Applicant: Google Inc.
Inventor： Daniel M. Keysers , Thomas Deselaers , Henry Allan Rowley
CPC classification number: G06K9/00422 , G06K9/00 , G06K9/00402 , G06K9/00865 , G06K9/52 , G06K2209/01
Abstract: An optimal recognition for handwritten input based on receiving a touch input from a user may be selected by applying both a delayed stroke recognizer as well as an overlapping recognizer to the handwritten input. A score may be generated for both the delayed stroke recognition as well as the overlapping recognition and the recognition corresponding to the highest score may be presented as the overall recognition.
Abstract translation: 可以通过将延迟的笔画识别器以及重叠识别器同时应用于手写输入来选择基于从用户接收触摸输入的手写输入的最佳识别。 可以为延迟的卒中识别以及重叠识别生成分数，并且对应于最高分数的识别可以被呈现为整体识别。
-
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.
-
公开(公告)号：US09262682B2
公开(公告)日：2016-02-16
申请号：US14645410
申请日：2015-03-11
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
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.
-
Patent Agency Ranking
公开(公告)号：US09213907B2
公开(公告)日：2015-12-15
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: G06K9/62 , G06K9/18 , G06K9/66 , G06T3/00 , G06K9/00 , G06Q20/22 , G06Q20/34 , G07F7/08 , G06K9/32
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：US20150235097A1
公开(公告)日：2015-08-20
申请号：US14184997
申请日：2014-02-20
Applicant: Google Inc.
Inventor： Li-Lun Wang , Thomas Deselaers , Henry Allan Rowley
CPC classification number: G06K9/00865 , G06K9/00402 , G06K9/222 , G06K9/342 , G06K9/4604 , G06K9/6256 , G06K9/66 , G06N99/005
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。
-
公开(公告)号：US20150186797A1
公开(公告)日：2015-07-02
申请号：US14145519
申请日：2013-12-31
Applicant: GOOGLE INC.
Inventor： Sanjiv Kumar , Henry Allan Rowley , Francisco Jose Claude Faust
IPC: G06N99/00
CPC classification number: G06N99/005
Abstract: A set S is initialized. Initially, S is empty; but, as the disclosed process is performed, items are added to it. It may contain one or more samples (e.g., items) from each class. One or more labeled samples for one or more classes may be obtained. A series of operations may be performed, iteratively, until a stopping criterion is reach to obtain the reduced set. For each class of the one or more classes, a point may be generated based on at least one sample in the class having a nearest neighbor in a set S with a different class label than the sample. The point may be added to the set S. The process may be repeated unless a stopping criterion is reached. A nearest neighbor for a submitted point in the set S may be identified and a candidate nearest neighbor may be output for the submitted point.
Abstract translation: 一组S被初始化。 最初，S是空的 但是，随着所公开的处理被执行，项目被添加到它。 它可以包含来自每个类的一个或多个样本（例如，项目）。 可以获得用于一个或多个类别的一个或多个标记的样品。 可以迭代地执行一系列操作，直到达到停止标准以获得缩减的集合。 对于一个或多个类的每个类，可以基于类中具有与样本不同的类标签的集合S中的最近邻的至少一个样本来生成点。 该点可以被添加到集合S中。除非达到停止标准，否则可以重复该过程。 可以识别集合S中的提交点的最近邻，并且可以为所提交的点输出候选最近邻。
-
公开(公告)号：US09070183B2
公开(公告)日：2015-06-30
申请号：US14059108
申请日：2013-10-21
Applicant: GOOGLE INC.
Inventor： Sanjiv Kumar , Henry Allan Rowley , Xiaohang Wang , Jose Jeronimo Moreira Rodrigues
IPC: G06K9/62 , G06T3/00 , G06K9/18 , G06K9/66 , G06K9/00 , G06Q20/22 , G06Q20/34 , G07F7/08 , G06K9/32
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：US08995741B2
公开(公告)日：2015-03-31
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
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。
-
-
-
-
-
-
-
-
-
Search Results
78
records
(took 0.036 seconds)
