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

arXiv: https://arxiv.org/abs/2504.04722 · HTML: https://mlsanity.com/paper/2504.04722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [A Survey of Vision-Language Pre-Trained Models](https://mlsanity.com/paper/2202.10936/) (2022)
- [Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization](https://mlsanity.com/paper/2401.13965/) (2024)
- [A Step towards Automated and Generalizable Tactile Map Generation using Generative Adversarial Networks](https://mlsanity.com/paper/2412.07191/) (2024)

## Built on by (most cited first, 3 of 3)

- [Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization Tool](https://mlsanity.com/paper/2604.09426/) (2026)
- [TactileEval: A Step Towards Automated Fine-Grained Evaluation and Editing of Tactile Graphics](https://mlsanity.com/paper/2604.19829/) (2026)
- [Text-based Tactile Graphics Generation for the Visually Impaired](https://mlsanity.com/paper/2607.22674/) (2026)

## Abstract (arXiv)

Tactile graphics are essential for providing access to visual information for the 43 million people globally living with vision loss. Traditional methods for creating these graphics are labor-intensive and cannot meet growing demand. We introduce TactileNet, the first comprehensive dataset and AI-driven framework for generating embossing-ready 2D tactile templates 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 graphics while reducing computational costs. Quantitative evaluations with tactile experts show 92.86% adherence to accessibility standards. Structural fidelity analysis revealed near-human design similarity, with an SSIM of 0.538 between generated graphics and expert-designed tactile images. Notably, our method preserves object silhouettes better than human designs (SSIM = 0.259 vs. 0.215 for binary masks), addressing a key limitation of manual tactile abstraction. The framework scales to 32,000 images (7,050 high-quality) across 66 classes, with prompt editing enabling customizable outputs (e.g., adding or removing details). By automating the 2D template generation step-compatible with standard embossing workflows-TactileNet accelerates production while preserving design flexibility. This work demonstrates how AI can augment (not replace) human expertise to bridge the accessibility gap in education and beyond. Code, data, and models will be publicly released to foster further research.