Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface

TL;DR

This paper presents an accessible text-to-image interface that leverages large language models to reduce typing effort, enhancing art creation accessibility for users with diverse abilities.

Contribution

It introduces a novel user interface for text-to-image generation that incorporates language model suggestions to improve accessibility, developed through iterative co-design.

Findings

The interface significantly reduces user typing effort.

Generative models support accessible art creation.

Positive user feedback on usability and accessibility.

Abstract

Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.