Generating scalable and semantically editable font representations

Patent metadata

Field	Value
Publication number	US-2022414314-A1
Application number	US-202117362031-A
Country	US
Kind code	A1
Filing date	Jun 29, 2021
Priority date	Jun 29, 2021
Publication date	Dec 29, 2022
Grant date	—

Abstract

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

First claim

What is claimed is:

1 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to:
determine a glyph label for a glyph utilizing a text recognition model; generate, from the glyph label and the glyph utilizing a glyph appearance propagation model, a font representation code for a font corresponding to the glyph; and generate, utilizing the glyph appearance propagation model, a glyph set from the font representation code and a set of glyph labels.

2 . The non-transitory computer readable medium of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate, utilizing a first neural network within the glyph appearance propagation model, parameters for a second neural network within the glyph appearance propagation model.

3 . The non-transitory computer readable medium of claim 2 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the parameters for the second neural network utilizing the first neural network by:
extracting a latent vector from the glyph label and the font representation code utilizing an encoder neural network within the first neural network; and generating weights and biases for the second neural network from the latent vector utilizing a plurality of decoder neural networks within the first neural network.

4 . The non-transitory computer readable medium of claim 3 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the glyph set by predicting values indicating glyph surfaces and background areas for a set of coordinate locations utilizing the second neural network according to the weights and biases.

5 . The non-transitory computer readable medium of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the font representation code for the font corresponding to the glyph by:
iteratively generating predicted glyphs from respective predicted font representation codes and the glyph label utilizing the glyph appearance propagation model; comparing the predicted glyphs to the glyph at respective iterations; and selecting, from the comparison and as the font representation code for the font corresponding to the glyph, a predicted font representation code corresponding to a predicted glyph that satisfies a similarity metric in relation to the glyph.

6 . The non-transitory computer readable medium of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the glyph set by utilizing the glyph appearance propagation model to generate a new glyph for each glyph label from the set of glyph labels according to the font representation code.

7 . The non-transitory computer readable medium of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
receive an indication from a client device of a modification to the glyph; and automatically propagate the modification to other glyphs within the glyph set by:
generating an updated font representation code for an updated font corresponding to the modified glyph; and
generating updated glyphs for the glyph set from the updated font representation code and the set of glyph labels.

8 . The non-transitory computer readable medium of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the computing device to generate the font representation code by utilizing the glyph appearance propagation model to generate a vector with a size corresponding to a plurality of anchor fonts and comprising indications of one or more of the plurality of anchor fonts contributing to a makeup of the font representation code.

9 . A system comprising:
one or more memory devices comprising a glyph and a glyph appearance propagation model; and one or more processors that are configured to cause the system to:
generate a font representation code for a font corresponding to the glyph by:
iteratively generating, utilizing the glyph appearance propagation model, predicted glyphs from a glyph label for the glyph and iteratively modified versions of a predicted font representation code;
comparing the glyph and the iteratively generated predicted glyphs; and
selecting, as the font representation code for the font and from the comparison, an iteratively modified version of the predicted font representation code corresponding to a predicted glyph that satisfies a similarity metric.

10 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to iteratively generate the predicted glyphs by utilizing the glyph appearance propagation model for a number of iterations to generate, for each iteration of the number of iterations, a respective predicted glyph from a respective version of the predicted font representation code and the glyph label.

11 . The system of claim 9 , wherein:
comparing the glyph and the iteratively generated predicted glyphs comprises determining, for each of the iteratively generated predicted glyphs, a loss between the glyph and the iteratively generated predicted glyph; and selecting the iteratively modified version of the predicted font representation code comprises selecting, from the comparison, an iteratively modified version of the predicted font representation code corresponding to a predicted glyph that satisfies a threshold loss.

12 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to generate the font representation code by utilizing the glyph appearance propagation model to generate a hybrid font representation code representing an interpolation between anchor fonts.

13 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to:
generate, utilizing the glyph appearance propagation model, a glyph set from the font representation code and a set of glyph labels; receive an indication from a client device to resize one or more glyphs of the glyph set to a larger scale; and resize, without degrading appearance, the one or more glyphs to the larger scale according to parameters of the glyph appearance propagation model learned via adaptive sampling along boundaries of sample glyphs.

14 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to:
receive an indication from a client device of a modified appearance to the glyph; and automatically propagate the modified appearance to other glyphs within a common glyph set utilizing the glyph appearance propagation model.

15 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to:
receive a partial glyph depicting an incomplete representation of a glyph; generate, from the partial glyph utilizing the glyph appearance propagation model, a different font representation code for a different font corresponding to the partial glyph; and generate, utilizing the glyph appearance propagation model, a completed glyph from the different font representation code and a glyph label for the partial glyph.

16 . The system of claim 9 , wherein the one or more processors are further configured to cause the system to utilize the glyph appearance propagation model comprisin

Assignees

• Adobe Inc

Inventors

• Zhang Zhifei
• Wang Zhaowen
• Jin Hailin
• Fisher Matthew

Classifications

• G06F40/109 Primary

  Font handling; Temporal or kinetic typography · CPC title

• G06T11/23

  using straight lines or curves · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06T11/203

  Physics · mapped topic

• G06N3/0454

  Physics · mapped topic