  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/.
