End-to-end semantically aware tactile generation
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".