Visualizing for the Non-Visual: Deep Learning to Enable Visually Impaired to Use Visualization

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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.
Publisher
Blackwell Publishing Inc.
Issue Date
2019-06
Language
English
Article Type
Article
Citation

Computer Graphics Forum, v.38, no.3, pp.249 - 260

ISSN
0167-7055
DOI
10.1111/cgf.13686
URI
http://hdl.handle.net/10203/279419
Appears in Collection
RIMS Journal Papers
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