Visualizing for the non-visual: Enabling the visually impaired to use visualization

Jinho Choi, Sanghun Jung, Deok Gun Park, Jaegul Choo, Niklas Elmqvist

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)249-260
Number of pages12
JournalComputer Graphics Forum
Issue number3
Publication statusPublished - 2019


  • Human-centered computing → Visual analytics
  • Visualization toolkits

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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