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: this https URL

从相似的图表图像中检索高质量的替代文本，以辅助用户生成准确的替代文本，并通过引入新的基准数据集、开发深度学习模型和设计用户界面来解决现有模型产生不准确或误导性信息的问题。