Early Accessibility: Automating Alt-Text Generation for UI Icons During App Development
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912333098385408 |
|---|---|
| author | Haque, Sabrina Csallner, Christoph |
| author_facet | Haque, Sabrina Csallner, Christoph |
| contents | Alt-text is essential for mobile app accessibility, yet UI icons often lack meaningful descriptions, limiting accessibility for screen reader users. Existing approaches either require extensive labeled datasets, struggle with partial UI contexts, or operate post-development, increasing technical debt. We first conduct a formative study to determine when and how developers prefer to generate icon alt-text. We then explore the ALTICON approach for generating alt-text for UI icons during development using two fine-tuned models: a text-only large language model that processes extracted UI metadata and a multi-modal model that jointly analyzes icon images and textual context. To improve accuracy, the method extracts relevant UI information from the DOM tree, retrieves in-icon text via OCR, and applies structured prompts for alt-text generation. Our empirical evaluation with the most closely related deep-learning and vision-language models shows that ALTICON generates alt-text that is of higher quality while not requiring a full-screen input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Early Accessibility: Automating Alt-Text Generation for UI Icons During App Development Haque, Sabrina Csallner, Christoph Software Engineering Human-Computer Interaction Alt-text is essential for mobile app accessibility, yet UI icons often lack meaningful descriptions, limiting accessibility for screen reader users. Existing approaches either require extensive labeled datasets, struggle with partial UI contexts, or operate post-development, increasing technical debt. We first conduct a formative study to determine when and how developers prefer to generate icon alt-text. We then explore the ALTICON approach for generating alt-text for UI icons during development using two fine-tuned models: a text-only large language model that processes extracted UI metadata and a multi-modal model that jointly analyzes icon images and textual context. To improve accuracy, the method extracts relevant UI information from the DOM tree, retrieves in-icon text via OCR, and applies structured prompts for alt-text generation. Our empirical evaluation with the most closely related deep-learning and vision-language models shows that ALTICON generates alt-text that is of higher quality while not requiring a full-screen input. |
| title | Early Accessibility: Automating Alt-Text Generation for UI Icons During App Development |
| topic | Software Engineering Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.13069 |