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/.

翻译：图表图像的替代文本对于使视力障碍和失明人士能够访问图形内容至关重要。传统上，替代文本由作者手动编写，但常常面临过度简化或复杂化的问题。近期趋势显示，人工智能已被用于替代文本生成。然而，现有模型容易产生不准确或误导性信息。我们通过从类似图表图像中检索高质量的替代文本来应对这一挑战，为用户创建替代文本时提供参考。我们的三项贡献如下：(1) 我们引入了一个包含5000张真实图像的新基准数据集，这些图像带有语义标注的高质量替代文本，收集自人机交互领域的相关出版物。(2) 我们开发了一个基于深度学习的模型，用于排序和检索具有相同视觉与文本语义的相似图表图像。(3) 我们设计了一个用户界面以简化替代文本的创建过程。我们的初步访谈和调查凸显了该用户界面的可用性。有关数据集及更多详细信息，请访问我们的项目页面：https://moured.github.io/alt4blind/。