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Non-Visually Performing Analytical Tasks on Statistical Charts.

Tomas Murillo-Morales1, Klaus Miesenberger1

  • 1Johannes Kepler University Linz, Austria.

Studies in Health Technology and Informatics
|September 7, 2017
PubMed
Summary
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This study introduces a natural language approach to make charts accessible. Blind users can now analyze bar charts using voice commands via a web interface.

Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Data Visualization

Background:

  • Chart accessibility remains a significant challenge for visually impaired individuals.
  • Current methods for chart interaction often lack intuitive, natural language interfaces.
  • Semantic annotation of visual elements is crucial for programmatic access.

Purpose of the Study:

  • To develop a natural language-based system for chart accessibility.
  • To enable blind users to interact with and analyze chart data through spoken queries.
  • To explore the use of formal underpinnings for semantic annotation in data visualization.

Main Methods:

  • Utilizing formal underpinnings to semantically annotate vector graphic elements.
  • Developing a natural language processing (NLP) pipeline for query interpretation.
Keywords:
AccessibilityOntologySmart GraphicsUser Interface

Related Experiment Videos

  • Implementing a web-based interface for user interaction.
  • Creating a prototype focusing on bar chart analysis.

    • Main Results:

    • Demonstrated a functional prototype enabling natural language interaction with charts.
    • Successfully allowed blind users to perform analytical tasks on bar charts.
    • Validated the effectiveness of semantic annotation for accessibility.

    Conclusions:

    • Natural language-based approaches significantly enhance chart accessibility for visually impaired users.
    • Semantic annotation provides a robust foundation for interactive data visualization.
    • The developed prototype shows promise for broader applications in accessible data analysis.