Abstract
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/.
O. Moured and S. A. Farooqui—Both authors contributed equally to this work.
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Acknowledgments
The authors would like to acknowledge the help of P. Venkatesh for his support in developing the UI. We would also like to thank the HoreKa computing cluster at KIT for the computing resources used to conduct this research.
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This research was funded by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreements no. 861166.
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Moured, O. et al. (2024). Alt4Blind: A User Interface to Simplify Charts Alt-Text Creation. In: Miesenberger, K., Peňáz, P., Kobayashi, M. (eds) Computers Helping People with Special Needs. ICCHP 2024. Lecture Notes in Computer Science, vol 14750. Springer, Cham. https://doi.org/10.1007/978-3-031-62846-7_35
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DOI: https://doi.org/10.1007/978-3-031-62846-7_35
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