Separation of projection and capture in collaboration environment

    公开(公告)号:US11425310B2

    公开(公告)日:2022-08-23

    申请号:US17144627

    申请日:2021-01-08

    Abstract: An optical shutter device includes: a projection shutter disposed in front of a projection device that projects an AR image onto an object surface with a local content; an imaging shutter disposed in front of an image capturing device that captures a local image of the local content; and a controller that electrically drives the projection and imaging shutters and switches the optical shutter device between a projection state and an imaging state. In the projection state, the controller electrically drives: the projection shutter to an open state, and the imaging shutter to a closed state. In the imaging state, the controller electrically drives: the projection shutter to the closed state, and the imaging shutter to the open state.

    GENERATION OF TRANSLATED ELECTRONIC DOCUMENT FROM AN INPUT IMAGE

    公开(公告)号:US20210097143A1

    公开(公告)日:2021-04-01

    申请号:US16586175

    申请日:2019-09-27

    Abstract: A method of generating an editable translated electronic document from an input image of an original document with a first layout includes: segmenting the input image to generate a first region including first untranslated text; extracting, from the first region, the first untranslated text and a first layout information; generating an editable output data including the first untranslated text and the first layout information; translating the first untranslated text into a translated text; editing the output data to include the translated text; and generating, using the first layout information, the translated electronic document including the translated text and a second layout that is identical to the first layout.

    AUTOMATIC GENERATION OF TRAINING DATA FOR HAND-PRINTED TEXT RECOGNITION

    公开(公告)号:US20230206674A1

    公开(公告)日:2023-06-29

    申请号:US17562344

    申请日:2021-12-27

    CPC classification number: G06V30/414 G06V30/1613 G06V30/2455 G06V30/19147

    Abstract: A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

    Automatic generation of training data for supervised machine learning

    公开(公告)号:US11270224B2

    公开(公告)日:2022-03-08

    申请号:US15941815

    申请日:2018-03-30

    Abstract: A method is disclosed for training a machine learning model to process electronic documents (EDs). The method includes obtaining a structured ED (SED) from a document repository, where the SED includes a first metadata. The method further generates, based on the SED, a bitmap and a second metadata. The method also determines whether the second metadata is within a predetermined threshold of the first metadata and generates, based on the SED and in response to determining that the second metadata is not within the predetermined threshold of the first metadata, a third metadata. The method additionally determines whether the third metadata is within the predetermined threshold of the first metadata and stores, in response to determining that the third metadata is within the predetermined threshold of the first metadata, a second SED comprising the bitmap and the third metadata.

    Automatic generation of training data for hand-printed text recognition

    公开(公告)号:US11715317B1

    公开(公告)日:2023-08-01

    申请号:US17562344

    申请日:2021-12-27

    CPC classification number: G06V30/414 G06V30/1613 G06V30/19147 G06V30/2455

    Abstract: A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

    SEPARATION OF PROJECTION AND CAPTURE IN COLLABORATION ENVIRONMENT

    公开(公告)号:US20220224817A1

    公开(公告)日:2022-07-14

    申请号:US17144627

    申请日:2021-01-08

    Abstract: An optical shutter device includes: a projection shutter disposed in front of a projection device that projects an AR image onto an object surface with a local content; an imaging shutter disposed in front of an image capturing device that captures a local image of the local content; and a controller that electrically drives the projection and imaging shutters and switches the optical shutter device between a projection state and an imaging state. In the projection state, the controller electrically drives: the projection shutter to an open state, and the imaging shutter to a closed state. In the imaging state, the controller electrically drives: the projection shutter to the closed state, and the imaging shutter to the open state.

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