Machine learning technologies for assessing text legibility in electronic documents

Patent metadata

Field	Value
Publication number	US-12299786-B2
Application number	US-202117390551-A
Country	US
Kind code	B2
Filing date	Jul 30, 2021
Priority date	Jul 31, 2020
Publication date	May 13, 2025
Grant date	May 13, 2025

Abstract

Systems and methods for using machine learning to assess text legibility in an electronic document are disclosed. According to certain aspects, an electronic device may train a machine learning model using training data that includes at least a representation of a text legibility level in the training data. Additionally, the electronic device may input the electronic document into the machine learning model, which may analyze the electronic document and output a representation of the text legibility level of a set of textual content included in the electronic document. The electronic device may display the output for review and assessment by a user, who may use the electronic device to facilitate any modifications to the electronic document.

First claim

What is claimed is:

1. A computer-implemented method of using machine learning to assess text legibility in an electronic document, the computer-implemented method comprising:
training, by a computer processor, a machine learning model using a training set of electronic documents, each of the training set of electronic documents comprising (i) a training set of textual content, (ii) a training set of visual content, and (iii) a training representation of a text legibility level of the training set of textual content;
storing the machine learning model in a memory;
accessing, by the computer processor, input data comprising an electronic document that comprises a set of textual content and a set of visual content;
analyzing, by the computer processor using the machine learning model, the input data;
based on the analyzing, generating, by the machine learning model, a red-green-blue (RGB) representation of a text legibility level of the set of textual content of the electronic document, the RGB representation comprising (i) a first digital image depicting, as a first plane, a set of detected characters, (ii) a second digital image depicting, as a second plane, a set of legibility values corresponding to the set of detected characters, and (iii) a third digital image depicting, as a third plane, a set of text lines;
generating, by the computer processor, a visual representation of the text legibility level of the set of textual content by combining the first plane, the second plane, and the third plane of the RBG representation; and
displaying, via a user interface, the visual representation of the text legibility level of the set of textual content, wherein a color and a composition of the visual representation indicates the text legibility level of the set of textual content.

2. The computer-implemented method of claim 1 , further comprising:
calculating, by the computer processor using the red-green-blue (RGB) representation, a measure of the text legibility level of the set of textual content.

3. The computer-implemented method of claim 2 , wherein calculating the measure of the text legibility level comprises:
generating, by the computer processor, a fourth image representing a difference between the second image of the set of legibility values and the first image of the set of detected characters, wherein the fourth image depicts a set of visual representations of a set of glyphs present in the set of textual content of the electronic document;
ascertaining, by the computer processor from the fourth image, at least a portion of the set of visual representations of the set of glyphs that do not meet a threshold characteristic;
modifying, by the computer processor, the fourth image by removing at least the portion of the set of visual representations; and
calculating, by the computer processor from the fourth image that was modified, the measure of the text legibility level of the set of textual content.

4. The computer-implemented method of claim 3 , wherein calculating, from the fourth image that was modified, the measure of the text legibility level comprises:
determining, by the computer processor, a ratio of a size of a remaining portion of the set of visual representations in the fourth image to a size of the electronic document; and
calculating, by the computer processor based on the ratio, the measure of the text legibility level of the set of textual content.

5. The computer-implemented method of claim 1 , wherein training the machine learning model comprises:
removing, from each of the training set of electronic documents, at least a portion of the training set of textual content; and
training, by the computer processor, the machine learning model using the training set of electronic documents with at least the portion of the training set of textual content removed therefrom.

6. A system for using machine learning to assess text legibility in an electronic document, comprising:
a memory storing a set of computer-readable instructions and data associated with a machine learning model; and
a processor interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the processor to:
train a machine learning model using a training set of electronic documents, each of the training set of electronic documents comprising (i) a training set of textual content, (ii) a training set of visual content, and (iii) a training representation of a text legibility level of the training set of textual content,
store the machine learning model in the memory,
access input data comprising an electronic document that comprises a set of textual content and a set of visual content,
analyze, using the machine learning model, the input data, and
based on the analyzing, generate, by the machine learning model, a red-green-blue (RGB) representation of a text legibility level of the set of textual content of the electronic document, the RGB representation comprising (i) a first digital image depicting, as a first plane, a set of detected characters, (ii) a second digital image depicting, as a second plane, a set of legibility values corresponding to the set of detected characters, and (iii) a third digital image depicting, as a third plane, a set of text lines,
generate a visual representation of the text legibility level by combining the first plane, the second plane, and the third plane of the RBG representation, and
display, via a user interface, the visual representation of the text legibility level, wherein a color and a composition of the visual representation indicates the text legibility level of the set of textual content.

7. The system of claim 6 , wherein the processor is configured to execute the set of computer-readable instructions to further cause the processor to:
calculate, using the red-green-blue (RGB) representation, a measure of the text legibility level of the set of textual content.

8. The system of claim 7 , wherein to calculate the measure of the text legibility level, the processor is configured to:
generate a fourth image representing a difference between the second image of the set of legibility values and the first image of the set of detected characters, wherein the fourth image depicts a set of visual representations of a set of glyphs present in the set of textual content of the electronic document,
ascertain, from the fourth image, at least a portion of the set of visual representations of the set of glyphs that do not meet a threshold characteristic,
modify the fourth image by removing at least the portion of the set of visual representations, and
calculate, from the fourth image that was modified, the measure of the text legibility level of the set of textual content.

9. The system of claim 8 , wherein to calculate, from the fourth image that was modified, the measure of the text legibility level, the processor is configured to:
determine a ratio of a size of a remaining portion of the set of visual representations in the fourth image to a size of the electronic document; and
calculate, based on the ratio, the measure of the text legibility level of the set of textual content.

10. The system of claim 6 , wherein to train the machine learning model, the processor is configured to:
remove, from each of the training set of electronic documents, at least a portion of the training set of textual content, and
train the machine learning model using the training set of electronic documents with at least the portion of the training set of textual content removed therefrom.

11. A non-transitory computer-readable storage medium configured to store instructions executable by a computer processor, the instruct

Assignees

• Cimpress Schweiz Gmbh

Inventors

• Nykyforov Vyacheslav

Classifications

• G06T11/26 Primary

  Drawing of charts or graphs · CPC title

• G06V30/10

  Character recognition · CPC title

• G06V10/22

  by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N20/00

  Machine learning · CPC title