AI Context-Based Alternative Text Generation
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Solution Overview
Problem
Conventional methods for generating alternative text for images on websites are error-prone and time-consuming, making many websites inaccessible to blind and visually-impaired users, as they rely on human input that often results in delays and inaccuracies.
Innovation Solution
The use of artificial intelligence techniques, including image captioning models, optical character recognition, emotion recognition libraries, and alternative text generation models, to automatically generate context-based alternative text, which provides a more accurate and efficient description of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human users manually enter ALT text for images, then websites become accessible to blind and visually-impaired users, but the process results in errors and delays
Solution Approach 1:
The system enables images to generate their own alternative text descriptions automatically through AI processing, eliminating the need for manual human input. The image captioning model processes the image data and generates context-based ALT text autonomously, resolving the contradiction by making the system self-sufficient rather than relying on external human operators.
Solution Approach 2:
The patent replaces the mechanical process of manual text entry with an automated AI-based image captioning system. The mechanical action of humans typing ALT text is substituted with computational processing using machine learning models that automatically generate descriptions, thereby eliminating both errors and delays associated with manual processes.
2Productivity
If websites use images to attract sighted users, then user engagement improves, but blind and visually-impaired users cannot effectively interpret these images
Solution Approach 1:
The patent introduces AI-generated alternative text as an intermediary between images and visually-impaired users. This mediator translates visual information into accessible text descriptions that screen readers can convey to blind users, enabling them to interpret images effectively while preserving the original visual content for sighted users.
Solution Approach 2:
The system changes the parameter of image accessibility by generating contextual text descriptions that convey the meaning and purpose of images to visually-impaired users. This parameter change transforms images from inaccessible visual elements into interpretable content through automated captioning, maintaining productivity for all user groups.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically generating context-based alternative text using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating text captions for an image derived from a web page by processing the image using an artificial intelligence-based image captioning model; determining context information pertaining to the image by processing the image using an artificial intelligence-based context and emotion recognition library; generating context-based alternative text for at least a portion of the image by processing, using at least one artificial intelligence-based alternative text generation model, at least a portion of one or more of the generated text caption(s) for the image and the determined context information pertaining to at least a portion of the image; and performing one or more automated actions based on the generated context-based alternative text.