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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.

Patent No.:

US 12288406 B1

PDFFull Text
Date of Patent:
Apr. 29, 2025

Filed:

Sep. 30, 2021

Utilizing machine-learning based object detection to improve optical character recognition

Applicant:

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

Inventors:

Ankit Bal, Noida, IN;

Mohit Gupta, Noida, IN;

Ram Bhushan Agrawal, Noida, IN;

Tarun Verma, New Delhi, IN;

Uttam Dwivedi, Noida, IN;

Assignee:

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

Attorney:

Keller Preece PLLC

Primary Examiner:

Feng Niu

Int. Cl.
CPC ...
G06T 9/00 (2005.12); G06N 3/0442 (2022.12); G06N 3/0455 (2022.12); G06N 3/0464 (2022.12); G06N 3/047 (2022.12); G06N 3/0475 (2022.12); G06N 3/084 (2022.12); G06N 5/01 (2022.12); G06N 7/01 (2022.12); G06N 20/00 (2018.12); G06N 20/10 (2018.12); G06N 20/20 (2018.12); G06T 3/60 (2005.12); G06V 30/148 (2021.12); G06V 30/162 (2021.12); G06V 30/262 (2021.12); G06V 30/413 (2021.12);
U.S. Cl.
CPC ...
G06V 30/153 (2021.12); G06N 3/0442 (2022.12); G06N 3/0455 (2022.12); G06N 3/0464 (2022.12); G06N 3/047 (2022.12); G06N 3/0475 (2022.12); G06N 3/084 (2012.12); G06N 5/01 (2022.12); G06N 7/01 (2022.12); G06N 20/00 (2018.12); G06N 20/10 (2018.12); G06N 20/20 (2018.12); G06T 3/60 (2012.12); G06V 30/15 (2021.12); G06V 30/162 (2021.12); G06V 30/262 (2021.12); G06V 30/413 (2021.12);
Abstract

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately enhancing optical character recognition with a machine learning approach for determining words from reverse text, vertical text, and atypically-sized text. For example, the disclosed systems segment a digital image into text regions and non-text regions utilizing an object detection machine learning model. Within the text regions, the disclosed systems can determine reverse text glyphs, vertical text glyphs, and/or atypically-sized text glyphs utilizing an edge based adaptive binarization model. Additionally, the disclosed systems can utilize respective modification techniques to manipulate reverse text glyphs, vertical text glyphs, and/or atypically-sized glyphs for analysis by an optical character recognition model. The disclosed systems can further utilize an optical character recognition model to determine words from the modified versions of the reverse text glyphs, the vertical text glyphs, and/or the atypically-sized text glyphs.

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