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    • 38-Issue 3
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    Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization

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    Date
    2019
    Author
    Choi, Jinho
    Jung, Sanghun ORCID
    Park, Deok Gun
    Choo, Jaegul ORCID
    Elmqvist, Niklas
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    Abstract
    The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep-neural-network-based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back-end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.
    BibTeX
    @article {10.1111:cgf.13686,
    journal = {Computer Graphics Forum},
    title = {{Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization}},
    author = {Choi, Jinho and Jung, Sanghun and Park, Deok Gun and Choo, Jaegul and Elmqvist, Niklas},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13686}
    }
    URI
    https://doi.org/10.1111/cgf.13686
    https://diglib.eg.org:443/handle/10.1111/cgf13686
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    • 38-Issue 3

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    Eurographics Association copyright © 2013 - 2023 
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    Theme by @mire NV
    System hosted at  Graz University of Technology.
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