TactileNet: Bridging the Accessibility Gap with AI-Generated Tactile Graphics for Individuals with Vision Impairment

Tactile graphics are essential for providing access to visual information for the 43 million people globally living with vision loss, as estimated by global prevalence data. However, traditional methods for creating these tactile graphics are labor-intensive and struggle to meet demand. We introduce TactileNet, the first comprehensive dataset and AI-driven framework for generating tactile graphics using text-to-image Stable Diffusion (SD) models. By integrating Low-Rank Adaptation (LoRA) and DreamBooth, our method fine-tunes SD models to produce high-fidelity, guideline-compliant tactile graphics while reducing computational costs. Evaluations involving tactile experts show that generated graphics achieve 92.86% adherence to tactile standards and 100% alignment with natural images in posture and features. Our framework also demonstrates scalability, generating 32,000 images (7,050 filtered for quality) across 66 classes, with prompt editing enabling customizable outputs (e.g., adding/removing details). Our work empowers designers to focus on refinement, significantly accelerating accessibility efforts. It underscores the transformative potential of AI for social good, offering a scalable solution to bridge the accessibility gap in education and beyond.