US 12,229,399 B2
Simulated handwriting image generator
Christopher Alan Tensmeyer, Provo, UT (US); Rajiv Jain, Vienna, VA (US); Curtis Michael Wigington, San Jose, CA (US); Brian Lynn Price, Pleasant Grove, UT (US); and Brian Lafayette Davis, Provo, UT (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Jan. 23, 2024, as Appl. No. 18/420,444.
Application 18/420,444 is a division of application No. 17/648,718, filed on Jan. 24, 2022, granted, now 11,899,927.
Application 17/648,718 is a continuation of application No. 16/701,586, filed on Dec. 3, 2019, granted, now 11,250,252, issued on Feb. 15, 2022.
Prior Publication US 2024/0168625 A1, May 23, 2024
Int. Cl. G06F 3/048 (2013.01); G06F 3/04883 (2022.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 30/226 (2022.01); G06V 30/228 (2022.01); G06V 30/32 (2022.01)
CPC G06F 3/04883 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06V 30/2264 (2022.01); G06V 30/2276 (2022.01); G06V 30/228 (2022.01); G06V 30/347 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for training a neural network to generate a digital image of simulated handwriting, the method comprising:
receiving a handwriting sample image having a training sequence of characters;
receiving a coded text input also having the training sequence of characters;
processing the handwriting sample image using an encoder neural network to produce a style description vector that is representative of the handwriting sample image;
processing the coded text input using a decoder neural network to produce a raw generated image of the coded text input, wherein the decoder neural network uses the style description vector to produce the raw generated image;
evaluating a first difference between the handwriting sample image and the raw generated image; and
modifying the encoder neural network and the decoder neural network to reduce the first difference.