Premium
Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization
Author(s) -
Choi Jinho,
Jung Sanghun,
Park Deok Gun,
Choo Jaegul,
Elmqvist Niklas
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13686
Subject(s) - computer science , visualization , leverage (statistics) , visually impaired , pipeline (software) , graphics , raster graphics , data visualization , artificial intelligence , human–computer interaction , computer graphics (images) , computer vision , information retrieval , programming language
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.