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.
@InProceedings{10.1007/978-3-031-62846-7_35,
author="Moured, Omar
and Farooqui, Shahid Ali
and M{\"u}ller, Karin
and Fadaeijouybari, Sharifeh
and Schwarz, Thorsten
and Javed, Mohammed
and Stiefelhagen, Rainer",
title="Alt4Blind: A User Interface to Simplify Charts Alt-Text Creation",
booktitle="Computers Helping People with Special Needs",
year="2024",
publisher="Springer Nature Switzerland",
isbn="978-3-031-62846-7"}