Alt4Blind: A User Interface to Simplify Charts Alt-Text Creation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Moured, Omar Farooqui, Shahid Ali Muller, Karin Fadaeijouybari, Sharifeh Schwarz, Thorsten Javed, Mohammed Stiefelhagen, Rainer |
| author_facet | Moured, Omar Farooqui, Shahid Ali Muller, Karin Fadaeijouybari, Sharifeh Schwarz, Thorsten Javed, Mohammed Stiefelhagen, Rainer |
| contents | Alternative Texts (Alt-Text) for chart images are essential for making graphics accessible to people with blindness and visual impairments. Traditionally, Alt-Text is manually written by authors but often encounters issues such as oversimplification or complication. Recent trends have seen the use of AI for Alt-Text generation. However, existing models are susceptible to producing inaccurate or misleading information. We address this challenge by retrieving high-quality alt-texts from similar chart images, serving as a reference for the user when creating alt-texts. Our three contributions are as follows: (1) we introduce a new benchmark comprising 5,000 real images with semantically labeled high-quality Alt-Texts, collected from Human Computer Interaction venues. (2) We developed a deep learning-based model to rank and retrieve similar chart images that share the same visual and textual semantics. (3) We designed a user interface (UI) to facilitate the alt-text creation process. Our preliminary interviews and investigations highlight the usability of our UI. For the dataset and further details, please refer to our project page: https://moured.github.io/alt4blind/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19111 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alt4Blind: A User Interface to Simplify Charts Alt-Text Creation Moured, Omar Farooqui, Shahid Ali Muller, Karin Fadaeijouybari, Sharifeh Schwarz, Thorsten Javed, Mohammed Stiefelhagen, Rainer Computer Vision and Pattern Recognition Human-Computer Interaction Alternative Texts (Alt-Text) for chart images are essential for making graphics accessible to people with blindness and visual impairments. Traditionally, Alt-Text is manually written by authors but often encounters issues such as oversimplification or complication. Recent trends have seen the use of AI for Alt-Text generation. However, existing models are susceptible to producing inaccurate or misleading information. We address this challenge by retrieving high-quality alt-texts from similar chart images, serving as a reference for the user when creating alt-texts. Our three contributions are as follows: (1) we introduce a new benchmark comprising 5,000 real images with semantically labeled high-quality Alt-Texts, collected from Human Computer Interaction venues. (2) We developed a deep learning-based model to rank and retrieve similar chart images that share the same visual and textual semantics. (3) We designed a user interface (UI) to facilitate the alt-text creation process. Our preliminary interviews and investigations highlight the usability of our UI. For the dataset and further details, please refer to our project page: https://moured.github.io/alt4blind/. |
| title | Alt4Blind: A User Interface to Simplify Charts Alt-Text Creation |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.19111 |