US 12,277,792 B2
Localized anomaly detection in digital documents using machine learning techniques
Atul Kumar, Bangalore (IN); Anamika Chatterjee, Kolkata (IN); and Saurabh Jha, Austin, TX (US)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Oct. 31, 2022, as Appl. No. 17/977,506.
Prior Publication US 2024/0144712 A1, May 2, 2024
Int. Cl. G06V 30/418 (2022.01); G06T 9/00 (2006.01); G06V 30/19 (2022.01); G06T 3/4046 (2024.01); G06V 10/764 (2022.01)
CPC G06V 30/418 (2022.01) [G06T 9/00 (2013.01); G06V 30/19093 (2022.01); G06T 3/4046 (2013.01); G06V 10/764 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
identifying at least one image in a digital document;
processing the identified at least one image using at least an image compression algorithm;
applying a first machine learning model to the processed at least one image, wherein the first machine learning model is trained to detect whether the processed at least one image comprises one or more modifications;
in response to detecting that the processed at least one image comprises at least one modification, applying a second machine learning model to identify a location in the processed at least one image corresponding to the at least one modification; and
generating an indication that identifies the location of the at least one modification in the processed at least one image;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.