Machine Learning Text Legibility Assessment
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Solution Overview
Problem
Existing digital design platforms lack the ability to automatically assess the legibility of textual content within digital designs, particularly when text is overlaid on background images, leading to potential frustration and the creation of designs with poor text legibility.
Innovation Solution
A computer-implemented method using machine learning to assess text legibility in electronic documents by training a machine learning model with a dataset of electronic documents that include textual and visual content, and then analyzing input data to output a representation of the text legibility level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users add text to background images in digital design platforms, then design creativity and customization are improved, but text legibility deteriorates
Solution Approach 1:
The system provides automated feedback to users about text legibility issues in their designs. The machine learning model analyzes the relationship between text and background images, identifies legibility problems, and presents this information to users so they can adjust their designs accordingly. This feedback mechanism allows users to maintain creative customization while improving text legibility through informed design adjustments.
2Measurement precision
If manual analysis of text legibility is performed, then assessment accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The patent replaces manual visual analysis with an automated machine learning system. The machine learning model processes digital designs and automatically assesses text legibility by analyzing the relationship between text elements and background images. This substitution of mechanical manual inspection with automated computational analysis achieves high assessment accuracy while significantly reducing time and resource requirements.
3Productivity
If automated text legibility assessment is implemented, then analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The system uses machine learning models that have been trained on extensive datasets of digital designs with known legibility characteristics. The model learns patterns and relationships between text and background elements, then applies these learned patterns to assess new designs. This approach enables automated efficient analysis by copying and generalizing from training data rather than requiring complex real-time computational geometry algorithms.
Data Source
AI summary
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.