Text-Guided Image-to-Image Translation for Tactile Map Generation
Bibliographic record
Abstract
Tactile graphics enable individuals with visual impairment to interpret visual information through touch, supporting navigation, education, and social engagement. However, manually designing tactile graphics is costly, labor-intensive, and difficult to scale. This work introduces a text-guided image-to-image translation approach to generate tactile maps from RGB maps. By leveraging natural language prompts, the method allows control over map elements such as lakes, rivers, and cities, enabling customization based on specific needs. To train the model, we created a custom dataset consisting of 1,845 RGB maps of Canadian provinces, each paired with multiple tactile variations reflecting different levels of detail. Corresponding text prompts were designed to describe these variations, forming a dataset of 9,800 triplets (RGB map, tactile map, prompt). Human expert assessments demonstrated that the proposed method outperforms a baseline model, with 47% of the outputs requiring minimal adjustments. The results highlight a scalable and efficient solution for tactile map generation, ensuring high-quality outputs while maintaining adaptability through text-based control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.013 | 0.006 |
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".