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By Dick Weisinger
Being able to access the internet and gather content from it has become a necessity for many aspects of our lives. But, if you are blind or in some other way visually impaired, being able to view web pages and read and interact with the page content can be extremely difficult. Part of the syntax of a web page include the capability of adding captions and descriptions to tag/label images to which help the visually impaired understand the page content, but many web sites have not yet added that capability.
Accessibility text is included in the web page by including alternative text (alt-text) to label and explain the content portrayed in images. Authoring concise alt-text to describe web pages images though can be very challenging. Imagine the difficulty there is in trying to descibe the layers of meaning that might be presented in a meme, for example.
Researchers are experimenting with the use of AI to help develop appropriate alt-text descriptions. Often the alt-text needs to be developed from the context of the purpose of the whole web page and not the isolated image to which it will be attached.
Mousumi Kapoor, the founder and CEO of Continual Engine, said that “the use of computer vision, NLP, machine learning, and AI can make complex documents and images readily accessible to people suffering from visual impairments. Traditionally, developers and designers have needed to manually make image content accessible by physically authoring meaningful alt-text (alternate text) for images. With the help of software products powered by AI, however, this process is automated, saving up to 60% of the time in comparison to doing the same processes manually. Besides enabling people with visual disabilities to get access to content that was earlier inaccessible to them, the use of such technologies helps in making content accessible to all.”
Abigale Stangl, University of Colorado Boulder alumna, said that “for alt-text to be accurate, both human and AI systems will need training to author image descriptions that are responsive or context-aware to the user’s information goal along with where the image is found.”