End-to-end semantically aware tactile generation

Tactile graphics are an essential tool for conveying visual information to visually impaired individuals. However, translating 2D plots, such as B’ezier curves, polygons, and bar charts, into an effective tactile format remains a challenge. This paper presents a novel, two-stage deep learning pipeline for automating this conversion process. Our method leverages a Pix2Pix architecture, employing a U-Net++ generator network for robust image generation. To improve the perceptual quality of the tactile representations, we incorporate an adversarial perceptual loss function alongside a gradient penalty. The pipeline operates in a sequential manner: firstly, converting the source plot into a grayscale tactile representation, followed by a transformation into a channel-wise equivalent. We evaluate the performance of our model on a comprehensive synthetic dataset consisting of 20,000 source-target pairs encompassing various 2D plot types. To quantify performance, we utilize fuzzy versions of established metrics like pixel accuracy, Dice coefficient, and Jaccard index. Additionally, a human study is conducted to assess the visual quality of the generated tactile graphics. The proposed approach demonstrates promising results, significantly streamlining the conversion of 2D plots into tactile graphics. This paves the way for the development of fully automated systems, enhancing accessibility of visual information for visually impaired individuals. • A Novel Two-Step Pipeline: We propose a novel two-step pipeline for generating editable tactile representations of statistical data charts. • We introduce a set of tailored evaluation metrics specifically designed for assessing the quality of tactile graphics. • We contribute to the domain by creating a new dataset of work-related tactile graphics.