Image Alt Text Generation Using Visual Analysis and NLP
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Solution Overview
Problem
Existing alt text for images is often insufficiently descriptive, failing to meet accessibility requirements and providing a suboptimal user experience for visually impaired users.
Innovation Solution
An image analysis is performed to generate data describing visual attributes, followed by natural language processing to create a text description, which is then evaluated and refined to produce contextually relevant and linguistically accurate alt text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple alt text is used for images, then the implementation is easy and quick, but the descriptiveness and accessibility quality deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising image analysis components and natural language processing components that mediate between the image and the alt text generation. This intermediary automatically analyzes image visual attributes and generates descriptive alt text, resolving the contradiction by providing high-quality descriptive text without requiring manual effort.
Solution Approach 2:
The system enables self-service by allowing the image itself to generate its own alt text through automated analysis of its visual attributes. The image analysis component extracts visual features and the NLP component formulates descriptive text autonomously, eliminating the need for external manual intervention while maintaining high descriptiveness.
2Loss of information
If manual alt text creation is performed to improve descriptiveness, then the accessibility quality improves, but the time consumption and productivity deteriorates
Solution Approach 1:
The patent replaces the mechanical process of manual alt text creation with an automated computational system. Image analysis algorithms and natural language processing models substitute human manual effort, generating descriptive alt text instantaneously rather than requiring time-consuming manual writing, thus improving productivity while maintaining descriptiveness.
Solution Approach 2:
The system changes the parameters of alt text generation by transitioning from manual text input to automated image attribute analysis. By analyzing visual attributes such as color, shape, and content, and transforming these parameters into natural language descriptions, the system achieves both high descriptiveness and rapid generation.
3Device complexity
If generic alt text is used, then the implementation is simple, but the contextual relevance and user experience deteriorates
Solution Approach 1:
The patent applies local quality by generating alt text that is specifically tailored to the unique visual attributes of each individual image. Rather than using generic templates, the system analyzes local visual characteristics such as specific colors, shapes, objects, and compositions present in each image, creating customized descriptive text that enhances contextual relevance.
Solution Approach 2:
The system performs preliminary action by pre-analyzing image visual attributes and pre-generating contextually relevant alt text before the text is needed. This advance processing ensures that the alt text is already optimized for contextual relevance and accessibility requirements, improving user experience without adding complexity during actual usage.
Data Source
AI summary
Generating alternative text (“alt text”) for images, including: performing an image analysis of an image to generate data describing one or more visual attributes of the image; performing a natural language processing on the data to generate a text description of the image; and generating, based on the text description of the image, an alt text for the image.