Machine print, hand print, and signature discrimination

    公开(公告)号:US10140510B2

    公开(公告)日:2018-11-27

    申请号:US15910797

    申请日:2018-03-02

    Applicant: Kofax, Inc.

    Abstract: Computer program products for discriminating hand and machine print from each other, and from signatures, are disclosed and include program code readable and/or executable by a processor to: receive an image, determine a color depth of the image; reducing the color depth of non-bi-tonal images to generate a bi-tonal representation of the image; identify a set of one or more graphical line candidates in either the bi-tonal image or the bi-tonal representation, the graphical line candidates including true graphical lines and/or false positives; discriminate any of the true graphical lines from any of the false positives; remove the true graphical lines from the bi-tonal image or the bi-tonal representation without removing the false positives to generate a component map comprising connected components and excluding graphical lines; identify one or more of the connected components in the component map; and output and/or display and indicator of each of the connected components.

    Machine print, hand print, and signature discrimination

    公开(公告)号:US09940511B2

    公开(公告)日:2018-04-10

    申请号:US14726335

    申请日:2015-05-29

    Applicant: Kofax, Inc.

    CPC classification number: G06K9/00422 G06K9/00187 G06K9/346

    Abstract: Systems, computer program products, and techniques for discriminating hand and machine print from each other, and from signatures, are disclosed and include determining a color depth of an image, the color depth corresponding to at least one of grayscale, bi-tonal and color; reducing color depth of non-bi-tonal images to generate a bi-tonal representation of the image; identifying a set of one or more graphical line candidates in either the bi-tonal image or the bi-tonal representation, the graphical line candidates including one or more of true graphical lines and false positives; discriminating any of the true graphical lines from any of the false positives; removing the true graphical lines from the bi-tonal image or the bi-tonal representation without removing the false positives to generate a component map comprising connected components and excluding graphical lines; and identifying one or more of the connected components in the component map.

    Mobile document detection and orientation based on reference object characteristics

    公开(公告)号:US09760788B2

    公开(公告)日:2017-09-12

    申请号:US14927359

    申请日:2015-10-29

    Applicant: Kofax, Inc.

    CPC classification number: G06K9/3208 G06K9/00463 G06K9/186

    Abstract: In various embodiments, methods, systems, and computer program products for detecting, estimating, calculating, etc. characteristics of a document based on reference objects depicted on the document are disclosed. In one approach, a computer-implemented method for processing a digital image depicting a document includes analyzing the digital image to determine one or more of a presence and a location of one or more reference objects; determining one or more geometric characteristics of at least one of the reference objects; defining one or more region(s) of interest based at least in part on one or more of the determined geometric characteristics; and detecting a presence or an absence of an edge of the document within each defined region of interest. Additional embodiments leverage the type of document depicted in the image, multiple frames of image data, and/or calculate or extrapolate document edges rather than locating edges in the image.

    SYSTEMS AND METHODS FOR ORGANIZING DATA SETS

    公开(公告)号:US20170140030A1

    公开(公告)日:2017-05-18

    申请号:US15422435

    申请日:2017-02-01

    Applicant: Kofax, Inc.

    CPC classification number: G06F17/30598 G06F17/30312 G06F17/3053 G06N99/005

    Abstract: According to one embodiment, a computer-implemented method for confirming/rejecting a most relevant example includes: generating a binary decision model by training a binary classifier using a plurality of training documents; classifying one or more test documents into one of a plurality of categories using the binary decision model, wherein the one or more test documents lack a user-defined category label; selecting a most relevant example of the classified test documents from among the classified test documents; displaying, using a display of the computer, the most relevant example of the classified test documents to a user; receiving, via the computer and from the user, a confirmation or a negation of a classification label of the most relevant example of the classified test documents; and storing the confirmation or the negation of the classification label of the most relevant example of the classified test documents to a memory of the computer.

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