AI-Enhanced Art Appreciation: Generating Text from Artwork to Promote Inclusivity


  • Tanisha Shende Oberlin College



Generative AI, Accessibility, Art, Human-computer Interaction, Visual processing


Visual art facilitates expression, communication, and connection, yet it remains inaccessible to those who are visually-impaired and those who lack the resources to understand the techniques and history of art. In this work, I propose the development of a generative AI model that generates a description and interpretation of a given artwork. Such research can make art more accessible, support art education, and improve the ability of AI to understand and translate between creative media. Development will begin with a formative study to assess the needs and preferences of blind and low vision people and art experts. Following the formative study, the basic approach is to train the model on a database of artworks and their accompanying descriptions, predict sentiments from extracted visual data, and generate a paragraph closely resembling training textual data and incorporating sentiment analysis. The model will then be evaluated quantitatively through metrics like METEOR and qualitatively through Turing tests in an iterative process.




How to Cite

Shende, T. (2024). AI-Enhanced Art Appreciation: Generating Text from Artwork to Promote Inclusivity. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23760-23762.