US 12,266,145 B1
Machine-learning models for image processing
Ashutosh K. Sureka, Irving, TX (US); Venkata Sesha Kiran Kumar Adimatyam, Irving, TX (US); Miriam Silver, Tel Aviv (IL); and Daniel Funken, Irving, TX (US)
Assigned to CITIBANK, N.A., New York, NY (US)
Filed by Citibank, N.A., New York, NY (US)
Filed on Apr. 8, 2024, as Appl. No. 18/629,259.
Int. Cl. G06V 10/25 (2022.01); G06T 7/13 (2017.01); G06V 20/70 (2022.01)
CPC G06V 10/25 (2022.01) [G06T 7/13 (2017.01); G06V 20/70 (2022.01); G06T 2207/30176 (2013.01); G06V 2201/07 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A method for capturing document imagery using object recognition bounding boxes, the method comprising:
obtaining, by a computer via one or more networks, image data depicting an object from a user device;
executing, by the computer, an object recognition engine of a machine-learning architecture using the image data as an input, the object recognition engine trained for detecting a type of document in the image data;
in response to detecting, by the computer, the object as a document of the type of document in the image data, generating, by the computer, a bounding box for the document according to pixel data corresponding to the document in the image data; and
generating, by the computer, for display at a user interface a dynamic alignment indicator based upon the bounding box for the document, wherein the computer generates a first label annotation of the bounding box and a second label annotation for the image data, the second label annotation corresponding to the bounding box.