Enhancing Accessibility: Automated Tactile Graphics Generation for Individuals with Visual Impairments

This study addresses the accessibility challenges faced by individuals with visual impairments due to limited access to graphic information, which significantly impacts their educational and social integration. Traditional methods for producing tactile graphics are labor-intensive and require specialized expertise, limiting their availability. Recent advancements in generative models, such as GANs, diffusion models, and VAEs, offer potential solutions to automate the creation of tactile images. In this work, we propose a novel generative model conditioned on text prompts, integrating a Bidirectional and Auto-Regressive Transformer (BART) and Vector Quantized Variational Auto-Encoder (VQ-VAE). This model transforms textual descriptions into tactile graphics, addressing key requirements for legibility and accessibility. The model’s performance was evaluated using cross-entropy, perplexity, mean square error, and CLIP Score metrics, demonstrating its ability to generate high-quality, customizable tactile images. Testing with educational and rehabilitation institutions confirmed the practicality and efficiency of the system, which significantly reduces production time and requires minimal operator expertise. The proposed approach enhances the production of inclusive educational materials, enabling improved access to quality education and fostering greater independence for individuals with visual impairments. Future research will focus on expanding the training dataset and refining the model for complex scenarios.