Generated Image Text Rendering for Legible Branded Content
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Solution Overview
Problem
Generative machine learning models struggle to produce legible text in generated images, limiting their application in contexts requiring branded or organization-specific content.
Innovation Solution
Trains a generative ML model on organization-specific data and uses an image-editing algorithm to identify legible locations and apply specified text with appropriate font style, size, and color, ensuring the text does not obscure significant elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generative machine learning model is used to create images, then image generation capability is improved, but text legibility in generated images deteriorates
Solution Approach 1:
The system separates image generation and text addition into two independent stages: first the generative model creates the base image, then a separate text rendering component adds legible text. This segmentation allows each component to optimize for its specific function without compromising the other.
Solution Approach 2:
An intermediary text rendering system is introduced between the generative model and the final output. This intermediary layer analyzes the generated image, determines optimal text placement and styling, and renders text that integrates seamlessly while maintaining legibility.
2Adaptability or versatility
If text is added to generated images, then branded content capability is improved, but image complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing the generated image to determine optimal text placement, font selection, and styling. The algorithm independently evaluates image features and makes decisions about text rendering without requiring manual intervention, thereby managing complexity through automation.
Solution Approach 2:
The system dynamically adjusts text rendering parameters (font size, color, position, style) based on the characteristics of the generated image. By changing these parameters adaptively, the system achieves branded content capability while managing complexity through parameter optimization rather than structural complexity.
3Loss of information
If text is placed in generated images, then information content is improved, but text legibility deteriorates due to image background complexity
Solution Approach 1:
The system applies local quality by analyzing different regions of the generated image and placing text in areas with optimal characteristics for readability. It evaluates local background properties (color, texture, complexity) and adapts text rendering parameters to each specific location, ensuring high legibility despite varying image backgrounds.
Solution Approach 2:
The system performs preliminary analysis of the generated image before text placement, identifying suitable regions and optimizing text parameters in advance. This preliminary action allows the system to select placement locations that maximize legibility while minimizing interference with image content, thereby preserving both information and readability.
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: (i) identifying, by a processor, a generative machine learning model trained on image data, (ii) generating, by the generative machine learning model executed by the processor, an image based on at least one parameter, (iii) editing, by an image-editing algorithm executed by the processor, the image to comprise a specified string of text in a selected area of the image, and (iv) causing display, by the processor, of the edited image.