AI powered alt-text remediation typically involves following steps - Image recognition, text extraction, object and scene descriptions, natural language processing (NLP), quality assessment, integration with content, manual review and editing, and bulk processing.

AI driven alt-text remediation can be effective for a wide range of images and non-text content, including:

• Photographs: AI can describe the content of photographs, including scenes, objects, people, and activities.
• Graphics and Diagrams: AI can analyze graphics and diagrams to provide descriptions of shapes, lines, colors, and the relationships between elements.
• Charts and Graphs: AI can generate alt text conveying the data and trends in charts and graphs.
• Logos and Icons: AI can identify and describe logos, icons, and symbols commonly used on websites and applications.
• Product Images: Ecommerce websites can benefit from AI-generated alt text for product images, describing products, features, and details.
• Infographics: AI analyzes and describes the content and structure of infographics, making them more accessible to users with visual impairments.
• Text within Images: AI can extract and provide alt text for text elements within images, such as signs, posters, or documents.
• Artwork and Illustrations: AI offers descriptions of artistic works, illustrations, and paintings.
• Screenshots: Alt text can be generated for screenshots, providing context for the captured content.
• Memes and Internet Culture: AI describes memes and images of Internet culture, enabling users to understand their humor or cultural significance.
• Animations and GIFs: While more challenging, some AI systems can generate alt text for animations and GIFs, describing their content and purpose.