Font recognition using adversarial neural network training
AssigneeAdobe Inc.
InventorsYang Liu, Zhaowen Wang, Hailin Jin
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Abstract
The present disclosure relates to a font recognition system that employs a multi-task learning framework and adversarial training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system adversarial trains a font recognition neural network by minimizing font classification loss while at the same time maximizing glyph classification loss. By employing an adversarially trained font classification neural network, the font recognition system can improve overall font recognition by removing the negative side effects from diverse glyph content.
Claims20
A system for training a neural network to classify digital fonts using supervised adversarial training comprising:
- a memory comprising:
- a plurality of digital fonts;
- and
- a plurality of glyphs;
- at least one processor;
- and
- at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
- generate a set of text images based on:
- selecting one or more glyphs from the plurality of glyphs,
- selecting a digital font from the plurality of digital fonts for each of the one or more glyphs, and
- rendering each of the one or more glyphs written in the selected digital font;
- generate a font recognition neural network configured to classify digital fonts;
- and
- train, based on the set of text images, the font recognition neural network using adversarial training by minimizing a loss for a font classifier while maximizing a loss for a glyph classifier to learn discriminative features that effectively differentiate digital fonts.
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:
- receive an input text image comprising an input digital font;
- determine a font probability vector for the input digital font using a font recognition neural network adversarially trained by minimizing a loss for a font classifier while maximizing a loss for a glyph classifier to learn discriminative features that effectively differentiate digital fonts;
- and
- identify the input digital font from known digital fonts based on the font probability vector for the input digital font.
In a digital medium environment for creating or editing electronic documents, a computer-implemented method of searching for and identifying images of digital fonts, comprising:
- receiving an input text image comprising an input digital font;
- determining a font probability vector for the input digital font using a font recognition neural network adversarially trained by minimizing a loss for a font classifier while maximizing a loss for a glyph classifier to learn discriminative features that effectively differentiate digital fonts;
- and
- identifying the input digital font from known digital fonts based on the font probability vector for the input digital font.
Dependencies are inferred from the claim text.