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Hunting for headings: sighted labeling vs. automatic classification of headings

Published:13 October 2008Publication History

ABSTRACT

Proper use of headings in web pages can make navigation more efficient for blind web users by indicating semantic divisions in the page. Unfortunately, many web pages do not use proper HTML markup (h1-h6 tags) to indicate headings, instead using visual styling to create headings, thus making the distinction between headings and other page text indistinguishable to blind users. In a user study in which sighted participants labeled headings on a set of web pages, participants did not often agree on which elements on the page should be labeled as headings, suggesting why headings are not used properly on the web today. To address this problem, we have created a system called HeadingHunter that predicts whether web page text semantically functions as a heading by examining visual features of the text as rendered in a web browser. Its performance in labeling headings compares favorably with both a manually-classified set of heading examples and the combined results of the sighted labelers in our study. The resulting system illustrates a general methodology of creating simple scripts operating over visual features that can be directly included in existing tools.

References

  1. Alexa web search -- data services, 2006. http://www.alexa.com.Google ScholarGoogle Scholar
  2. C. Asakawa and H. Takagi. Web Accessibility: A Foundation for Research, chapter Transcoding. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Bechhofer, S. Harper, and D. Lunn. Sadie: Semantic annotation for accessibility. In Proceedings of 5th International Semantic Web Conference, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Bigham, A. C. Cavender, J. T. Brudvik, J. O. Wobbrock, and R. Ladner. Webinsitu: A comparative analysis of blind and sighted browsing behavior. In Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '07). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Bigham, R. S. Kaminsky, R. Ladner, O. M. Danielsson, and G. L. Hempton. Webinsight: Making web images accessible. In Proceedings of 8th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '06), 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Bigham and R. Ladner. Accessmonkey: A collaborative scripting framework for web users and developers. In Proceedings of the International Cross-Disciplinary Conference on Web Accessibility (W4A '07), pages 25--34, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. L. Fleiss. Measuring nominal scale agreement among many raters. In Psychological Bulletin, Vol. 76, No. 5 pp. 378--382Google ScholarGoogle ScholarCross RefCross Ref
  8. W. Gatterbauer, P. Bohunsky, M. Herzog, B. Kroepl, and B. Pollak. Towards domain--independent information extraction from web tables. In Proceedings of the 16th International Conference on the World Wide Web (WWW '07), pages 71--80, Banff, Alberta, Canada, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Greasemonkey firefox extension. http://greasemonkey.mozdev.org/.Google ScholarGoogle Scholar
  10. H69: Providing heading elements at the beginning of each section of content - techniques for wcag 2.0.Google ScholarGoogle Scholar
  11. World Wide Web Consortium, 2008. http://www.w3.org/WAI/GL/WCAG20/WD-WCAG20-TECHS/H69.html.Google ScholarGoogle Scholar
  12. Html techniques for web content accessibility guidelines 1.0. World Wide Web Consortium, November 2000. http://www.w3.org/TR/WCAG10-HTML-TECHS/.Google ScholarGoogle Scholar
  13. JAWS 8.0 for windows. Freedom Scientific, 2006. http://www.freedomscientific.com.Google ScholarGoogle Scholar
  14. J. Mankoff, H. Fait, and T. Tran. Is your web page accessible?: a comparative study of methods for assessing web page accessibility for the blind. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '05), pages 41--50, New York, NY, USA, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. Miyashita, D. Sato, H. Takagi, and C. Asakawa. Aibrowser for multimedia: introducing multimedia content accessibility for visually impaired users. In Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility (ASSETS '07), pages 91---98, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. H. Petrie, C. Harrison, and S. Dev. Describing images on the web: a survey of current practice and prospects for the future. In Proceedings of Human Computer Interaction International (HCII '05), July 2005.Google ScholarGoogle Scholar
  17. M. Pilgrim, editor. Greasemonkey Hacks: Tips & Tools for Remixing the Web with Firefox. O'Reilly Media, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81--106. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. I. Ramakrishnan, A. Stent, and G. Yang. Hearsay: Enabling audio browsing on hypertext content. In Proceedings of the 13th International Conference on the World Wide Web (WWW '04), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Understanding WCAG 2.0. World Wide Web Consortium, 2007. http://www.w3.org/TR/2007/WD-UNDERSTANDING-WCAG20-20071211/navigation-mechanisms-headings.html.Google ScholarGoogle Scholar
  21. T. Watanabe. Experimental evaluation of usability and accessibility of heading elements. In Proceedings of the International Cross-Disciplinary Conference on Web Accessibility (W4A '07), pages 157 -- 164, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Web content accessibility guidelines 2.0 (WCAG 2.0).Google ScholarGoogle Scholar
  23. World Wide Web Consortium, 2006. http://www.w3.org/TR/WCAG20/.Google ScholarGoogle Scholar
  24. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2nd edition, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. World Wide Web Consortium. The global structure of an HTML document, 2008. http://www.w3.org/TR/html401/struct/global.html.Google ScholarGoogle Scholar
  26. Y. Yang and H. Zhang. Html page analysis based on visual cues. In Proceedings of the 6th International Conference on Document Analysis and Recognition (ICDAR '01), pages 859--864, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. W. Zhang and A. T.L. Using artificial neural networks to identify headings in newspaper documents. In Proceedings of the International Joint Conference on Neural Networks, 2003, volume 3, pages 2283--2287. IEEE, 20--24 July 2003.Google ScholarGoogle ScholarCross RefCross Ref
  28. Document Object Model (DOM) Level 3 Core Specification. http://www.w3.org/TR/2004/REC-DOM-Level-3-Core-20040407/.Google ScholarGoogle Scholar

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          cover image ACM Conferences
          Assets '08: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
          October 2008
          332 pages
          ISBN:9781595939760
          DOI:10.1145/1414471

          Copyright © 2008 ACM

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          Publication History

          • Published: 13 October 2008

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