US 12,327,422 B2
Method for identifying handwritten characters on an image using a classification model
Raphael Fitzgerald, Winston-Salem, NC (US)
Assigned to Truist Bank, Charlotte, NC (US)
Filed by Truist Bank, Charlotte, NC (US)
Filed on Jul. 28, 2022, as Appl. No. 17/815,706.
Application 17/815,706 is a continuation of application No. 17/661,654, filed on May 2, 2022, granted, now 12,205,390.
Prior Publication US 2023/0351784 A1, Nov. 2, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 30/226 (2022.01); G06N 3/088 (2023.01)
CPC G06V 30/226 (2022.01) [G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
13. A method for identifying handwritten characters on an image, said method comprising:
reading and converting typed text on the image to machine encoded text to identify locations of the typed text on the image;
identifying a location on the image that includes handwritten text based on the location of predetermined typed text on the image;
identifying clusters of non-white pixels in the image at the location having the handwritten text, where each cluster is presumed to be a handwritten character, wherein identifying clusters of non-white pixels includes employing a constraint that requires all of the clusters to be within a certain percentage size of each other;
generating an individual and separate cluster image for each identified cluster,
classifying each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and
determining what character each cluster image is based on the classification.