Template-based key-value extraction for inferring OCR key values within form images

    公开(公告)号:US11495011B2

    公开(公告)日:2022-11-08

    申请号:US16988536

    申请日:2020-08-07

    Abstract: The system has a form analysis module that receives an image of a form into which values have been filled for the possible fields of information on the form, such as first name, address, age, and the like. By using a library of form templates, a form analysis module allows both flexibility of form processing and simplicity for the user. That is, the techniques used by the form analysis module allow the processing of any form image for which the library has a form template example. The form image need not precisely match any form template, but rather may be scaled or shifted relative to a corresponding template. Additionally, the user need only provide the form image itself, without providing any additional exemplars, metadata for training, or the like.

    SYSTEMS AND METHODS FOR HIERARCHICAL MULTI-LABEL CONTRASTIVE LEARNING

    公开(公告)号:US20220300761A1

    公开(公告)日:2022-09-22

    申请号:US17328779

    申请日:2021-05-24

    Abstract: Embodiments described herein provide a hierarchical multi-label framework to learn an embedding function that may capture the hierarchical relationship between classes at different levels in the hierarchy. Specifically, supervised contrastive learning framework may be extended to the hierarchical multi-label setting. Each data point has multiple dependent labels, and the relationship between labels is represented as a hierarchy of labels. The relationship between the different levels of labels may then be learnt by a contrastive learning framework.

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