Making STEM Subjects Graphs Accessible for Blind and Visually Impaired Students Using Document Understanding Transformer (DONUT) Model.

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Bibliographic Details
Title:	Making STEM Subjects Graphs Accessible for Blind and Visually Impaired Students Using Document Understanding Transformer (DONUT) Model.
Authors:	Hassan Zaidi, Syed Muhammad¹ m.hassan@smiu.edu.pk, Khan, Abdul Hafeez¹ akhan@smiu.edu.pk, Shaikh, Sarmad Ahmed¹ sarmad@smiu.edu.pk, Hussain, Imtiaz¹ imtiaz@smiu.edu.pk
Source:	International Journal of Online & Biomedical Engineering. 2025, Vol. 21 Issue 14, p4-19. 16p.
Subjects:	Assistive technology, People with visual disabilities, Vision disorders, Visual aids, Image processing, Data extraction, Web accessibility, Engineering
Abstract:	Most STEM (Science, Technology, Engineering, and Mathematics) subjects rely heavily on graphs and charts, which remain largely inaccessible to students who are blind or visually impaired. While text can often be made accessible through screen readers, complex visual structures such as charts are much harder to interpret non-visually. This study presents a proof-of-concept system that applies the DONUT (Document Understanding Transformer) model to STEM charts. The model was trained and evaluated on the Benetech STEM dataset and tested on multiple images, demonstrating promising results in extracting key information such as chart type, chart ID, and x-y coordinate values. Although no user-centered trials or formal educational studies have yet been conducted, this work establishes an initial technical foundation for converting chart data into accessible formats. By enabling interpretation of chart types and data trends, the proposed system has the potential to improve accessibility in STEM education for blind and visually impaired learners, pending further validation and integration with assistive technologies. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Most STEM (Science, Technology, Engineering, and Mathematics) subjects rely heavily on graphs and charts, which remain largely inaccessible to students who are blind or visually impaired. While text can often be made accessible through screen readers, complex visual structures such as charts are much harder to interpret non-visually. This study presents a proof-of-concept system that applies the DONUT (Document Understanding Transformer) model to STEM charts. The model was trained and evaluated on the Benetech STEM dataset and tested on multiple images, demonstrating promising results in extracting key information such as chart type, chart ID, and x-y coordinate values. Although no user-centered trials or formal educational studies have yet been conducted, this work establishes an initial technical foundation for converting chart data into accessible formats. By enabling interpretation of chart types and data trends, the proposed system has the potential to improve accessibility in STEM education for blind and visually impaired learners, pending further validation and integration with assistive technologies. [ABSTRACT FROM AUTHOR]
ISSN:	26268493
DOI:	10.3991/ijoe.v21i14.58675