Steering Generative Models for Accessibility: EasyRead Image Generation

TL;DR

This paper presents a method to generate EasyRead pictograms using fine-tuned diffusion models, aiming to improve accessibility by reducing manual design effort and ensuring stylistic consistency.

Contribution

We introduce a unified pipeline for EasyRead pictogram generation by fine-tuning Stable Diffusion with LoRA adapters and propose an EasyRead score for quality benchmarking.

Findings

Diffusion models can generate coherent EasyRead pictograms.

Fine-tuning improves stylistic stability and consistency.

The pipeline enables scalable, accessible pictogram production.

Abstract

EasyRead pictograms are simple, visually clear images that represent specific concepts and support comprehension for people with intellectual disabilities, low literacy, or language barriers. The large-scale production of EasyRead content has traditionally been constrained by the cost and expertise required to manually design pictograms. In contrast, automatic generation of such images could significantly reduce production time and cost, enabling broader accessibility across digital and printed materials. However, modern diffusion-based image generation models tend to produce outputs that exhibit excessive visual detail and lack stylistic stability across random seeds, limiting their suitability for clear and consistent pictogram generation. This challenge highlights the need for methods specifically tailored to accessibility-oriented visual content. In this work, we present a unified pipeline for generating EasyRead pictograms by fine-tuning a Stable Diffusion model using LoRA adapters on a curated corpus that combines augmented samples from multiple pictogram datasets. Since EasyRead pictograms lack a unified formal definition, we introduce an EasyRead score to benchmark pictogram quality and consistency. Our results demonstrate that diffusion models can be effectively steered toward producing coherent EasyRead-style images, indicating that generative models can serve as practical tools for scalable and accessible pictogram production.