US 12,249,133 B2
Systems and methods for image modification and image based content capture and extraction in neural networks
Christopher Dale Lund, San Diego, CA (US); and Sreelatha Samala, Los Altos, CA (US)
Assigned to Open Text Corporation, Menlo Park, CA (US)
Filed by Open Text Corporation, Waterloo (CA)
Filed on Mar. 7, 2023, as Appl. No. 18/179,485.
Application 18/179,485 is a continuation of application No. 16/991,776, filed on Aug. 12, 2020, granted, now 11,625,810.
Application 16/991,776 is a continuation of application No. 16/229,397, filed on Dec. 21, 2018, granted, now 10,776,903, issued on Sep. 15, 2020.
Application 16/229,397 is a continuation in part of application No. 16/035,307, filed on Jul. 13, 2018, granted, now 10,902,252, issued on Jan. 26, 2021.
Claims priority of provisional application 62/533,576, filed on Jul. 17, 2017.
Prior Publication US 2023/0206619 A1, Jun. 29, 2023
Int. Cl. G06K 9/00 (2022.01); A61K 35/12 (2015.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 5/046 (2023.01); G06T 3/4046 (2024.01); G06T 5/92 (2024.01); G06V 10/22 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 20/62 (2022.01); G06V 30/14 (2022.01); G06V 30/18 (2022.01); G06V 30/19 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06V 30/10 (2022.01)
CPC G06V 10/82 (2022.01) [G06F 18/24143 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06T 3/4046 (2013.01); G06T 5/92 (2024.01); G06V 10/22 (2022.01); G06V 10/454 (2022.01); G06V 20/62 (2022.01); G06V 30/1444 (2022.01); G06V 30/18057 (2022.01); G06V 30/19173 (2022.01); G06V 30/413 (2022.01); G06V 30/414 (2022.01); G06V 30/10 (2022.01)] 18 Claims
OG exemplary drawing
 
1. An image modification system comprising:
a processor; and
a non-transitory computer readable medium storing instructions that are executable by the processor for:
obtaining an input image from a device communicatively connected to the image recognition system over a network;
determining a predetermined feature type of the obtained input image;
generating, using a fully convolutional neural network, a first heat map, the first heat map indicating, for each pixel in the input image, a probability that the pixel forms a portion of a particular image component of the predetermined feature type, in the input image, wherein the particular image component of the predetermined feature type comprises a document component that includes a document background element;
modifying intensities of at least a subset of the pixels in the input image according to the first heat map to produce a modified image, wherein the subset of pixels corresponds to either high or low generated probabilities such that each respective pixel of the subset of pixels is either lightened or darkened based on having either a high generated probability or a low generated probability, with respective colors of the modified pixels remaining the same before and after modifying the intensities of the modified pixels; and
storing the modified image in a memory.