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公开(公告)号：US11783610B2
公开(公告)日：2023-10-10
申请号：US17726793
申请日：2022-04-22
Applicant: Adobe Inc.
Inventor： Ashutosh Mehra , Md Nadeem Akhtar , Pranav Kumar
IPC: G06K9/00 , G06V30/413 , G06N20/00 , G06V30/412 , G06V30/414 , G06V10/82 , G06V10/44
CPC classification number: G06V30/413 , G06N20/00 , G06V10/454 , G06V10/82 , G06V30/412 , G06V30/414
Abstract: A method comprises determining instance bounds associated with each of one or more structural elements in a document using a machine learning model. The method further comprises determining an error in the instance bounds associated with a particular one of the one or more structural elements. The method further comprises correcting the error in the instance bounds associated with the particular structural element using document content associated with the particular structural element, thereby generating corrected instance bounds associated with the particular structural element. The method further comprises generating a structural map of the document based on the corrected instance bounds.
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公开(公告)号：US20230230406A1
公开(公告)日：2023-07-20
申请号：US17577605
申请日：2022-01-18
Applicant: ADOBE INC.
Inventor： Ashutosh Mehra , Christopher Alan Tensmeyer , Vlad Ion Morariu , Jiuxiang Gu
IPC: G06V30/412 , G06N20/20 , G06F40/174
CPC classification number: G06V30/412 , G06N20/20 , G06F40/174
Abstract: Methods and systems are provided for facilitating identification of fillable regions and/or data associated therewith. In embodiments, a candidate fillable region indicating a region in a form that is a candidate for being fillable is obtained. Textual context indicating text from the form and spatial context indicating positions of the text within the form are also obtained. Fillable region data associated with the candidate fillable region is generated, via a machine learning model, using the candidate fillable region, the textual context, and the spatial context. Thereafter, a fillable form is generated using the fillable region data, the fillable form having one or more fillable regions for accepting input.
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公开(公告)号：US11227159B2
公开(公告)日：2022-01-18
申请号：US16876540
申请日：2020-05-18
Applicant: Adobe Inc.
Inventor： Rajiv Bhawanji Jain , Vlad Ion Morariu , Vitali Petsiuk , Varun Manjunatha , Ashutosh Mehra , Vicente Ignacio Ordonez Roman
Abstract: Introduced here are computer programs and associated computer-implemented techniques for creating visualizations to explain the outputs produced by models designed for object detection. To accomplish this, a graphics editing platform can obtain a reference output that identifies a region of pixels in a digital image that allegedly contains an object. Then, the graphics editing platform can compute the similarity between the reference output and a series of outputs generated by a model upon being applied to masked versions of the digital image. A visualization component can be produced based on the similarity.
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公开(公告)号：US20240212367A1
公开(公告)日：2024-06-27
申请号：US18145392
申请日：2022-12-22
Applicant: Adobe Inc.
Inventor： Punit Singh , Shawn Alan Gaither , Leonard Rosenthol , Jayant Vaibhav Srivastava , Ashutosh Mehra
Abstract: Techniques for text identification in layered digital content are described. In an implementation, an item of digital content is received including a plurality of layers. A text layer is identified in the item of digital content from the plurality of layers. A text channel image is generated by isolating the text layer from the plurality of layers. A text identification is then generated based on the text channel image using a page decomposition model, the page decomposition model trained using machine learning.
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公开(公告)号：US11508173B2
公开(公告)日：2022-11-22
申请号：US16669144
申请日：2019-10-30
Applicant: ADOBE INC.
Inventor： Ashutosh Mehra , Vlad Ion Morariu , Kajal Gupta , Jayant Vaibhav Srivastava , Curtis Michael Wigington , Tushar Tiwari
IPC: G06K9/00 , G06V30/414 , G06N3/02 , G06K9/62 , G06N20/00
Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.
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Patent Agency Ranking
公开(公告)号：US11880648B2
公开(公告)日：2024-01-23
申请号：US17456143
申请日：2021-11-22
Applicant: ADOBE INC.
Inventor： Aparna Garimella , Sumit Shekhar , Bhanu Prakash Reddy Guda , Vinay Aggarwal , Vlad Ion Morariu , Ashutosh Mehra
IPC: G06F40/174 , G06F40/30 , G06F40/284 , G06N3/045
CPC classification number: G06F40/174 , G06F40/284 , G06F40/30 , G06N3/045
Abstract: Embodiments provide systems, methods, and computer storage media for extracting semantic labels for field widgets of form fields in unfilled forms. In some embodiments, a processing device accesses a representation of a fillable widget of a form field of an unfilled form. The processing device generates an encoded input representing text and layout of a sequence of tokens in a neighborhood of the fillable widget. The processing device uses a machine learning model to extract a semantic label representing a field type of the fillable widget in view of the encoded input. The processing device causes execution of an action using the semantic label.
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公开(公告)号：US11321559B2
公开(公告)日：2022-05-03
申请号：US16655365
申请日：2019-10-17
Applicant: Adobe Inc.
Inventor： Ashutosh Mehra , Md Nadeem Akhtar , Pranav Kumar
Abstract: Techniques are disclosed for identifying document structural elements and correcting errors in the classification and/or location of the identified structural elements. An example method includes determining location and classification for a structural element on a page of the document using a machine learning (ML) model; determining one or more errors in the location and/or classification for the structural element; and correcting each instance of the one or more errors using other content in the document (e.g., content spatially adjacent to the corresponding structural element on the page of the document). The method may further include storing the document and the location and classification (as corrected), and/or generating a structural map of the page of the document based on the location and classification (as corrected). The use of the document content to correct errors greatly enhances the agreement between the identified structural elements and the original document.
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8.发明公开公开(公告)号：US20240161529A1
公开(公告)日：2024-05-16
申请号：US18055752
申请日：2022-11-15
Applicant: Adobe Inc.
Inventor： Vlad Morariu , Puneet Mathur , Rajiv Jain , Ashutosh Mehra , Jiuxiang Gu , Franck Dernoncourt , Anandhavelu N , Quan Tran , Verena Kaynig-Fittkau , Nedim Lipka , Ani Nenkova
IPC: G06V30/413 , G06V10/82
CPC classification number: G06V30/413 , G06V10/82
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.
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公开(公告)号：US20220245958A1
公开(公告)日：2022-08-04
申请号：US17726793
申请日：2022-04-22
Applicant: Adobe Inc.
Inventor： Ashutosh Mehra , Md Nadeem Akhtar , Pranav Kumar
IPC: G06V30/413 , G06N20/00 , G06V30/412 , G06V30/414
Abstract: A method comprises determining instance bounds associated with each of one or more structural elements in a document using a machine learning model. The method further comprises determining an error in the instance bounds associated with a particular one of the one or more structural elements. The method further comprises correcting the error in the instance bounds associated with the particular structural element using document content associated with the particular structural element, thereby generating corrected instance bounds associated with the particular structural element. The method further comprises generating a structural map of the document based on the corrected instance bounds.
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公开(公告)号：US20210357644A1
公开(公告)日：2021-11-18
申请号：US16876540
申请日：2020-05-18
Applicant: Adobe Inc.
Inventor： Rajiv Bhawanji Jain , Vlad Ion Morariu , Vitali Petsiuk , Varun Manjunatha , Ashutosh Mehra , Vicente Ignacio Ordonez Roman
Abstract: Introduced here are computer programs and associated computer-implemented techniques for creating visualizations to explain the outputs produced by models designed for object detection. To accomplish this, a graphics editing platform can obtain a reference output that identifies a region of pixels in a digital image that allegedly contains an object. Then, the graphics editing platform can compute the similarity between the reference output and a series of outputs generated by a model upon being applied to masked versions of the digital image. A visualization component can be produced based on the similarity.
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