Neural Network Text Placement in Images
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Solution Overview
Problem
Existing technologies face challenges in aesthetically placing text within images, particularly when the image content and formatting are variable, making it difficult to achieve legible and pleasing text placement without obscuring critical content.
Innovation Solution
A neural network system is trained to identify candidate text placement areas within images, generating a mask and defining a bounding box for optimal text placement, which includes using a heatmap to determine suitable areas and avoiding placement over important image content, and optionally creating a uniform background fill when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text is placed randomly within images, then text placement is simple and fast, but text may obscure critical image content and diminish the overall effect
Solution Approach 1:
The patent introduces a neural network model as an intermediary between the text and image. This model generates a heatmap that identifies suitable text placement regions, acting as a mediator that guides text positioning away from critical image content while maintaining aesthetic quality and legibility
Solution Approach 2:
The system performs preliminary analysis by generating a heatmap that pre-identifies optimal text placement regions before actual text is positioned. This preliminary action allows the system to avoid critical image content and plan text placement strategically, improving both quality and efficiency
2Reliability
If text placement follows aesthetic guidelines, then text placement quality improves, but the complexity of determining placement increases
Solution Approach 1:
The patent replaces complex manual or rule-based text placement mechanisms with a neural network model. This substitution allows the system to learn aesthetic guidelines and placement patterns automatically from training data, reducing the need for explicit complex rules while maintaining high placement quality
Solution Approach 2:
The system transforms the text placement problem into a parameter-based solution by generating a heatmap that encodes placement suitability across different image regions. This parameter transformation allows the complex aesthetic judgment to be represented as numerical values that guide text positioning
3Measurement precision
If a neural network is trained to identify text placement areas, then text placement accuracy improves, but computational resources and training data requirements increase
Solution Approach 1:
The patent segments the text placement problem into two distinct components: (1) generating a heatmap that identifies suitable regions, and (2) positioning text within those regions. This segmentation allows the neural network to focus on region identification while simpler logic handles text positioning, reducing overall computational requirements
Data Source
AI summary
Based on a received digital image and text, a neural network trained to identify candidate text placement areas within images may be used to generate a mask for the digital image that includes a candidate text placement area. A bounding box for the digital image may be defined for the text and based on the candidate text placement area, and the text may be superimposed onto the digital image within the bounding box.