HCCVMar 14

Steering Generative Models for Accessibility: EasyRead Image Generation

arXiv:2603.1369532.9h-index: 5
Predicted impact top 58% in HC · last 90 daysOriginality Incremental advance
AI Analysis

This addresses the need for scalable and cost-effective pictogram production to support accessibility for people with intellectual disabilities, low literacy, or language barriers, representing an incremental improvement in applying generative models to a specific domain.

The paper tackled the problem of automatically generating EasyRead pictograms, which are simple images for accessibility, by fine-tuning a Stable Diffusion model with LoRA adapters on curated datasets, resulting in effective steering of the model to produce coherent EasyRead-style images.

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.

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