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dc.contributor.authorChoi, Jinhoen_US
dc.contributor.authorJung, Sanghunen_US
dc.contributor.authorPark, Deok Gunen_US
dc.contributor.authorChoo, Jaegulen_US
dc.contributor.authorElmqvist, Niklasen_US
dc.contributor.editorGleicher, Michael and Viola, Ivan and Leitte, Heikeen_US
dc.description.abstractThe 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectVisualization toolkits
dc.titleVisualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualizationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersAnalysis Techniques

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  • 38-Issue 3
    EuroVis 2019 - Conference Proceedings

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