US 2021/0334666 A1 · MACHINE LEARNING-BASED INFERENCE OF GRANULAR FONT PROPERTIES

Abstract

A textual properties model is used to infer values for certain font properties of interest given certain text-related data, such as rendered text images. The model may be used for numerous purposes, such as aiding with document layout, identifying font families that are similar to a given font families, and generating new font families with specific desired properties. In some embodiments, the model is trained from a combination of synthetic data that is labeled with values for the font properties of interest, and partially-labeled data from existing “real-world” documents.

Claims20

Dependent claims

A computer-implemented method for inference of font properties based on rendered text, the computer-implemented method comprising:

obtaining a rendered text image of text in a document;
obtaining a machine learning model trained to infer values for a plurality of font properties based on rendered text images;
and
using the machine learning model to infer values for font properties of a font family of the rendered text.

1 more element

Dependencies are inferred from the claim text.

Facts

Application

16/854,913Filed 22 Apr 2020

Published

28 Oct 2021

Term ends

Not estimatedThe source records don’t support an estimate.

Classification20 codes

G06N 5/02
Computing arrangements using knowledge-based models: knowledge representation; Symbolic representation
G06F 17/16
Complex mathematical operations: matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
G06F 40/109
Formatting, i.e. changing of presentation of documents: font handling; Temporal or kinetic typography
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks

Machine learning-based inference of granular font properties

Life of this patent

Machine learning-based inference of granular font properties

Life of this patent

Abstract

Claims20