Automated Text Placement in Digital Images Using Contrast Analysis
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Solution Overview
Problem
Manual processes for incorporating text into promotional images are inefficient and not scalable, especially when localizing content for multiple languages and dialects, as they require labor-intensive placement to avoid obscuring visually interesting areas and ensure adequate contrast.
Innovation Solution
An automated system that determines candidate text-allowed regions within a digital image using edge detection and facial recognition, calculates suitability scores, and modifies the image to place text without overlapping visually interesting areas or reserved regions, using a network infrastructure with content servers and endpoint devices to distribute and process localized promotional images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual processes are used for text placement in promotional images, then text can be carefully positioned to avoid obscuring visually interesting areas, but the process becomes labor-intensive and inefficient for localization
Solution Approach 1:
The system enables automated text placement by allowing the computer to autonomously analyze the base image, identify suitable regions using edge detection and facial recognition, calculate suitability scores, and position text without requiring manual human intervention for each localization instance
Solution Approach 2:
The manual mechanical process of visual inspection and text positioning is replaced with an automated computational system that uses edge detection algorithms, facial recognition, and suitability score calculations to determine optimal text placement locations
2Reliability
If manual text placement is performed for each localized version, then quality control can be maintained, but the time and labor required increase substantially
Solution Approach 1:
The system performs preliminary analysis of the base image by detecting edges, identifying facial regions, and determining text-allowed regions before text placement, creating a structured framework that ensures quality while enabling rapid automated processing of multiple localized versions
Solution Approach 2:
The system calculates suitability scores based on multiple parameters including edge density, facial region proximity, and contrast metrics to objectively determine optimal text placement locations, replacing subjective manual quality assessment with quantifiable automated evaluation
3Productivity
If automated methods are used for text placement, then productivity increases, but reliability and quality control may deteriorate
Solution Approach 1:
The system incorporates contrast analysis that evaluates the suitability of text placement locations by analyzing the contrast between the text-containing image and the base image background, providing automated quality feedback to ensure text visibility and aesthetic quality without manual intervention
Data Source
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AI summary
One embodiment of the present invention sets forth a technique for incorporating a text-containing image into a digital. The technique includes analyzing a digital image to determine one or more text-allowed regions included in the digital image, and, for each of the one or more text-allowed regions, computing a numeric value based on a color contrast between pixels of a text-containing image and pixels of the text-allowed region, wherein the text-containing image is to be incorporated into one of the text-allowed regions included in the digital image. The technique further includes selecting a first text-allowed region based at least in part on the numeric value computed for each text-allowed region, and incorporating the text-containing image into the first text-allowed region included in the digital image.