Abstract
Exam documents are essential educational materials for exam preparation. However, they pose a significant academic barrier for blind and visually impaired students, as they are often created without accessibility considerations. Typically, these documents are incompatible with screen readers, contain excessive white space, and lack alternative text for visual elements. This situation frequently requires intervention by experienced sighted individuals to modify the format and content for accessibility. We propose ACCSAMS, a semi-automatic system designed to enhance the accessibility of exam documents. Our system offers three key contributions: (1) creating an accessible layout and removing unnecessary white space, (2) adding navigational structures, and (3) incorporating alternative text for visual elements that were previously missing. Additionally, we present the first multilingual manually annotated dataset, comprising 1,293 German and 900 English exam documents which could serve as a good training source for deep learning models.
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Acknowledgments
The authors thank Viola Buck Cabrera and Iva Andreeva for the implementation of the end-user web application. In addition, we 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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Wilkening, D., Moured, O., Schwarz, T., Müller, K., Stiefelhagen, R. (2024). ACCSAMS: Automatic Conversion of Exam Documents to Accessible Learning Material for Blind and Visually Impaired. 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_39
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DOI: https://doi.org/10.1007/978-3-031-62846-7_39
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