INTELLIGENT AND PAPERLESS OFFICE
    2.
    发明申请
    INTELLIGENT AND PAPERLESS OFFICE 有权
    智能无纸办公室

    公开(公告)号:US20090119324A1

    公开(公告)日:2009-05-07

    申请号:US11933544

    申请日:2007-11-01

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30011

    摘要: The claimed subject matter provides a system and/or a method that facilitates collecting and organizing electronic documents. An interface component can receive a document. A manager component can automatically file the document into a category based at least in part upon a portion of static metadata associated with the document and a portion of metadata dynamically generated from an inference related to the portion of static metadata associated with the document.

    摘要翻译: 所要求保护的主题提供了便于收集和组织电子文档的系统和/或方法。 接口组件可以接收文档。 管理者组件可以至少部分地基于与该文档相关联的静态元数据的一部分以及从与该文档相关联的静态元数据部分相关的推断动态生成的元数据的一部分来自动将该文档写入类别。

    Intelligent and paperless office
    4.
    发明授权
    Intelligent and paperless office 有权
    智能无纸办公室

    公开(公告)号:US08676806B2

    公开(公告)日:2014-03-18

    申请号:US11933544

    申请日:2007-11-01

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30011

    摘要: The claimed subject matter provides a system and/or a method that facilitates collecting and organizing electronic documents. An interface component can receive a document. A manager component can automatically file the document into a category based at least in part upon a portion of static metadata associated with the document and a portion of metadata dynamically generated from an inference related to the portion of static metadata associated with the document.

    摘要翻译: 所要求保护的主题提供了便于收集和组织电子文档的系统和/或方法。 接口组件可以接收文档。 管理者组件可以至少部分地基于与该文档相关联的静态元数据的一部分以及从与该文档相关联的静态元数据部分相关的推断动态生成的元数据的一部分来自动将该文档写入类别。

    Logical structure layout identification and classification for offline character recognition
    5.
    发明授权
    Logical structure layout identification and classification for offline character recognition 有权
    逻辑结构布局识别和离线字符识别分类

    公开(公告)号:US07844114B2

    公开(公告)日:2010-11-30

    申请号:US11299873

    申请日:2005-12-12

    IPC分类号: G06K9/18

    CPC分类号: G06K9/80

    摘要: A method and system for implementing character recognition is described herein. An input character is received. The input character is composed of one or more logical structures in a particular layout. The layout of the one or more logical structures is identified. One or more of a plurality of classifiers are selected based on the layout of the one or more logical structures in the input character. The entire character is input into the selected classifiers. The selected classifiers classify the logical structures. The outputs from the selected classifiers are then combined to form an output character vector.

    摘要翻译: 本文描述了用于实现字符识别的方法和系统。 接收到一个输入字符。 输入字符由特定布局中的一个或多个逻辑结构组成。 识别一个或多个逻辑结构的布局。 基于输入字符中的一个或多个逻辑结构的布局来选择多个分类器中的一个或多个。 整个字符被输入到所选择的分类器中。 所选分类器对逻辑结构进行分类。 然后将所选分类器的输出组合以形成输出字符向量。

    Activity detector
    8.
    发明授权
    Activity detector 有权
    活动检测器

    公开(公告)号:US07386171B2

    公开(公告)日:2008-06-10

    申请号:US11845588

    申请日:2007-08-27

    申请人: Patrice Y. Simard

    发明人: Patrice Y. Simard

    IPC分类号: G06K9/46

    CPC分类号: G06K9/00456

    摘要: A system and method facilitating activity (e.g., dithering/half toning and/or noise) detection is provided. The invention includes an activity detection system having a connected component analyzer and an activity detector. The invention provides for the quantity of connected component(s) in and/or intersecting a region surrounding a pixel to be determined. The activity detector provides an activity map output based, at least in part, upon the quantity of connected component(s) in and/or intersecting the region. The invention further provides for an optional image processor. In one example, if the quantity exceeds a first threshold, dithering/half toning is detected and appropriate action can be taken. Additionally, if the quantity is less than a second threshold, noise is detected and appropriate action can be taken.

    摘要翻译: 提供了促进活动(例如,抖动/半色调和/或噪声)检测的系统和方法。 本发明包括具有连接分量分析器和活动检测器的活动检测系统。 本发明提供在要确定的像素周围的区域中和/或相交的连接分量的量。 活动检测器至少部分地基于在区域中和/或与该区域相交的连接分量的量来提供活动图输出。 本发明还提供了一种可选的图像处理器。 在一个示例中,如果数量超过第一阈值,则检测到抖动/半色调,并且可以采取适当的动作。 此外,如果数量小于第二阈值,则检测噪声并且可以采取适当的动作。

    System and method facilitating pattern recognition
    9.
    发明授权
    System and method facilitating pattern recognition 有权
    系统和方法促进模式识别

    公开(公告)号:US07286699B2

    公开(公告)日:2007-10-23

    申请号:US11327913

    申请日:2006-01-09

    IPC分类号: G06K9/00 G06K9/62

    摘要: A system and method facilitating pattern recognition is provided. The invention includes a pattern recognition system having a convolutional neural network employing feature extraction layer(s) and classifier layer(s). The feature extraction layer(s) comprises convolutional layers and the classifier layer(s) comprises fully connected layers. The pattern recognition system can be trained utilizing a calculated cross entropy error. The calculated cross entropy error is utilized to update trainable parameters of the pattern recognition system.

    摘要翻译: 提供了一种促进模式识别的系统和方法。 本发明包括具有使用特征提取层和分类器层的卷积神经网络的模式识别系统。 特征提取层包括卷积层,分类层包括完全连接的层。 可以使用计算的交叉熵误差来训练模式识别系统。 计算的交叉熵误差用于更新模式识别系统的可训练参数。

    System and method for accelerating and optimizing the processing of machine learning techniques using a graphics processing unit
    10.
    发明授权
    System and method for accelerating and optimizing the processing of machine learning techniques using a graphics processing unit 有权
    用于加速和优化使用图形处理单元的机器学习技术的处理的系统和方法

    公开(公告)号:US07219085B2

    公开(公告)日:2007-05-15

    申请号:US10837382

    申请日:2004-04-30

    IPC分类号: G06F15/80

    CPC分类号: G06K9/00986 G06N3/08

    摘要: A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.

    摘要翻译: 一种用于处理机器学习技术(例如神经网络)和使用图形处理单元(GPU)来加速和优化处理的其他非图形应用的系统和方法。 该系统和方法传输一种可用于从CPU到GPU的各种机器学习技术的架构。 处理到GPU的转移是通过克服这些限制并在GPU架构的框架内工作良好的几种新技术实现的。 由于克服了这些限制,机器学习技术特别适用于GPU上的处理,因为GPU通常比典型的CPU功能更强大。 此外,类似于图形处理,机器学习技术的处理涉及解决非平凡解决方案和大量数据的问题。