Image quality assessment and improvement for performing optical character recognition

    公开(公告)号:US10366309B2

    公开(公告)日:2019-07-30

    申请号:US16138669

    申请日:2018-09-21

    Applicant: INTUIT INC.

    Abstract: Techniques are disclosed for performing optical character recognition (OCR) by assessing and improving quality of electronic documents to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a reference image of the electronic document, distorting the reference image by adjusting different sets of one or more parameters associated with a quality of the reference image to generate a plurality of distorted images, analyzing each distorted image to detect the adjusted set of parameters and corresponding adjusted values, determining an accuracy of detection of the set of parameters and the adjusted values, and training a model based at least on the plurality of distorted images and the accuracy of the detection, wherein the trained model determines at least a first technique for adjusting a set of parameters in a second image to prepare the second image for optical character recognition.

    Image quality assessment and improvement for performing optical character recognition

    公开(公告)号:US10108883B2

    公开(公告)日:2018-10-23

    申请号:US15337285

    申请日:2016-10-28

    Applicant: INTUIT INC.

    Abstract: Techniques are disclosed for performing optical character recognition (OCR) by assessing and improving quality of electronic documents to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a reference image of the electronic document, distorting the reference image by adjusting different sets of one or more parameters associated with a quality of the reference image to generate a plurality of distorted images, analyzing each distorted image to detect the adjusted set of parameters and corresponding adjusted values, determining an accuracy of detection of the set of parameters and the adjusted values, and training a model based at least on the plurality of distorted images and the accuracy of the detection, wherein the trained model determines at least a first technique for adjusting a set of parameters in a second image to prepare the second image for optical character recognition.

    SYSTEM AND METHOD FOR PREDICTING SUPPORT ESCALATION

    公开(公告)号:US20210248617A1

    公开(公告)日:2021-08-12

    申请号:US16785964

    申请日:2020-02-10

    Applicant: Intuit Inc.

    Abstract: A method and system train an analysis model with a machine learning process to predict whether a current user of the data management system will contact customer assistance agents of the data management system. The machine learning process utilizes historical clickstream data indicating actions taken by a plurality of historical users of the data management system while using the data management system. The analysis model predicts whether the current user will contact customer assistance agents by analyzing current clickstream data associated with the current user.

    Image quality assessment and improvement for performing optical character recognition

    公开(公告)号:US11030477B2

    公开(公告)日:2021-06-08

    申请号:US16431555

    申请日:2019-06-04

    Applicant: INTUIT INC.

    Abstract: Techniques are disclosed for performing optical character recognition (OCR) by assessing and improving quality of electronic documents to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a reference image of the electronic document, distorting the reference image by adjusting different sets of one or more parameters associated with a quality of the reference image to generate a plurality of distorted images, analyzing each distorted image to detect the adjusted set of parameters and corresponding adjusted values, determining an accuracy of detection of the set of parameters and the adjusted values, and training a model based at least on the plurality of distorted images and the accuracy of the detection, wherein the trained model determines at least a first technique for adjusting a set of parameters in a second image to prepare the second image for optical character recognition.

    COMBINING STATISTICAL METHODS WITH A KNOWLEDGE GRAPH

    公开(公告)号:US20210158144A1

    公开(公告)日:2021-05-27

    申请号:US16693593

    申请日:2019-11-25

    Applicant: INTUIT INC.

    Abstract: Certain aspects of the present disclosure provide techniques for node matching with accuracy by combining statistical methods with a knowledge graph to assist in responding (e.g., providing content) to a user query in a user support system. In order to provide content, a keyword matching algorithm, statistical method (e.g., a trained BERT model), and data retrieval are each implemented to identify node(s) in a knowledge graph with encoded content relevant to the user's query. The implementation of the keyword matching algorithm, statistical method, and data retrieval results in a matching metric score, semantic score, and graph metric data, respectively. Each score associated with a node is combined to generate an overall score that can be used to rank nodes. Once the nodes are ranked, the top ranking nodes are displayed to the user for selection. Based on the selection, content encoded in the node is displayed to the user.

    Mapping natural language utterances to nodes in a knowledge graph

    公开(公告)号:US11989214B2

    公开(公告)日:2024-05-21

    申请号:US17513460

    申请日:2021-10-28

    Applicant: INTUIT INC.

    CPC classification number: G06F16/3329 G06F40/30 G06N5/02 G10L15/063

    Abstract: Certain aspects of the present disclosure provide techniques for mapping natural language to stored information. The method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application associated with a set of topics and providing the natural language utterance to a natural language model configured to identify nodes of a knowledge graph. The method further includes, based on output of the natural language model, identifying a node of a knowledge graph associated with the natural language utterance, wherein the output of the natural language model includes a node identifier for the node of the knowledge graph and providing the node identifier to the knowledge engine. The method further includes receiving a response associated with the node of the knowledge graph from the knowledge engine and transmitting the response to the user in response to the long-tail query.

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