Textual Design Agent for Background-Adaptive Layout
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current design prediction engines struggle to generate relevant, diverse, and personalized text designs for electronic documents due to the subjective nature of design choices and unbalanced data distribution, often recommending the most popular fonts and sizes without considering the background image characteristics or optimal text placement.
Innovation Solution
An intelligent system that breaks down the text design process into specialized components for recommending text location, color, font, and size using machine-learning models, such as convolutional neural networks, to generate diverse and relevant designs by combining these characteristics into a whole design evaluation, ensuring the text is visible and compatible with the background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If heuristic rules and pre-defined templates are used for design prediction, then the design process is simple to implement, but the design results lack diversity and personalization
Solution Approach 1:
The design prediction system is segmented into multiple independent recommendation modules, each responsible for predicting a specific design parameter (text location, color, font, size). Each module uses specialized machine learning models trained on specific aspects of design data, allowing the system to handle complexity through modular decomposition while maintaining high adaptability through personalized predictions for each parameter.
2Adaptability or versatility
If machine-learning models are used to generate diverse and personalized designs, then design relevance and diversity improve, but the system complexity increases
Solution Approach 1:
The complex machine learning system is divided into separate recommendation modules for different design parameters. Each module is a specialized model trained on specific data related to that parameter, making the overall system more manageable and easier to implement while achieving high personalization through the combined output of these specialized models.
3Reliability
If text design recommendations are based on popular fonts and sizes, then the recommendations are safe and commonly accepted, but the designs lack creativity and fail to consider background characteristics
Solution Approach 1:
The system applies local quality analysis by examining the specific characteristics of each background image (such as color, texture, content) and adjusting text design recommendations accordingly. Instead of using universal popular fonts and sizes, the system adapts text properties to match the local characteristics of the background, ensuring optimal compatibility and aesthetic quality for each specific design context.
Data Source
AI summary
Embodiments of the technology described herein, are an intelligent system that aims to expedite a text design process by providing text design predictions interactively. The system works with a typical text design scenario comprising a background image and one or more text strings as input. In the design scenario, the text string is to be placed on top of the background. The textual design agent may include a location recommendation model that recommends a location on the background image to place the text. The textual design agent may also include a font recommendation model, a size recommendation model, and a color recommendation model. The output of these four models may be combined to generate draft designs that are evaluated as a whole (combination of color, font, and size) for the best designs. The top designs may be output to the user.