The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 12, 2022

Filed:

Oct. 17, 2019
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Verena Sabine Kaynig-Fittkau, Cambridge, MA (US);

Smitha Bangalore Naresh, Sudbury, MA (US);

Shawn Alan Gaither, Raleigh, NC (US);

Richard Cohn, Newton, MA (US);

Paul John Asente, Redwood City, CA (US);

Eylon Stroh, San Carlos, CA (US);

Emily Seminerio, Medfield, MA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 30/413 (2022.01); G06N 20/00 (2019.01); G06V 30/412 (2022.01); G06V 30/414 (2022.01);
U.S. Cl.
CPC ...
G06V 30/413 (2022.01); G06N 20/00 (2019.01); G06V 30/412 (2022.01); G06V 30/414 (2022.01);
Abstract

Techniques are provided for identifying structural elements of a document. One Methodology includes generating a first channel of rasterized content by rasterizing a full page of the document and generating one or more additional channels of rasterized content from the page of the document by rasterizing one or more corresponding content types from the page of the document. Each of the one or more additional channels includes a specific type of content that is different from each of the other one or more additional channels. The methodology further includes inputting the first channel of rasterized content and the one or more additional channels of rasterized content into a machine learning (ML) model. The methodology continues with determining location and classification for each of a plurality of structural elements on the page of the document using the ML model.


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