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Making Web Images Accessible

Making Web Images Accessible. Jeffrey P. Bigham Richard Ladner, Ryan Kaminsky, Gordon Hempton, Oscar Danielsson University of Washington Computer Science & Engineering. Browsing while blind. Screen readers Images cannot be read W3C accessibility standards

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Making Web Images Accessible

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  1. Making Web Images Accessible Jeffrey P. Bigham Richard Ladner, Ryan Kaminsky, Gordon Hempton, Oscar Danielsson University of Washington Computer Science & Engineering

  2. Browsing while blind • Screen readers • Images cannot be read • W3C accessibility standards • “Provide a text equivalent for every non-text element” • What if no alternative text? • Nothing • Filename (060315_banner_253x100.gif) • Link address (http://www.cs.washington.edu)

  3. nav_svcs.gif

  4. Outline • Web Studies • Providing Labels • WebInSight System • Evaluation • Future Work

  5. Web Studies: All Images != • Significant images need alternative text • Informative • alt, title, and longdesc HTML attributes • Insignificant images need empty alt text • Automatic Determination? <img src=“graph.gif” alt=“sales graph” title=“sales graph” longdesc=“sales_descrip.txt”> <img src="images/spacer.gif" width="1" height="1"> <img src="images/spacer.gif" width="1" height="1“ alt=“”> • More than one color AND both dimensions > 10 pixels • An associated action (clickable, etc.)

  6. Web Studies • Previous studies • img tags with defined alt attribute: • 27.9%[1], 47.7%[2], and 49.4%[2] • Significant images have a defined alt attribute? • 76.9%[3] • Gaps • Some Ignore Image Significance • Some Ignore Image Importance [1] T. C. Craven. “Some features of alt text associated with images in web pages.” (Information Research, Volume 11, 2006). [2] Luis von Ahn et al. “Improving accessibility of the web with a computer game.” (CHI 2006) [3] Helen Petrie et al. “Describing images on the web: a survey of current practice and prospects for the future.” (HCII 2005)

  7. Web Studies • University of Washington CSE Department Traffic • ~1 week • 11,989,898 images. • 40.8% significant • 63.2% alt text Significant images with alternative text. Significant images without alternative text.

  8. Study Results Percentage of significant images provided alternative text, pages with over 90% of significant images provided alternative text, number of web sites in group, and number of images examined.

  9. Outline • Web Studies • Providing Labels • WebInSight System • Evaluation • Future Work

  10. Providing Labels: Context Labeling • Many important images are links • Linked page often describes image • What happens if you click <a href=“p234.htm”><img src=“p523.gif”></a> <a href=“p234.htm”><img src=“p523.gif” alt=“People of UW”></a> <html> <head> <title>People of UW</title> <body> <h1>People</h1> … </body> </html>

  11. Providing Labels: OCR Labeling (Optical Character Recognition) Improves recognition 25% relative to base OCR! [4] Jain et al. “Automatic text location in images and video frames.” (ICPR 1998)

  12. Providing Labels: Human Labeling [5] [6] • Humans are best • Recent games compel accurate labeling • WebInSight database has over 10,000 images • Could do this on demand [5] Ahn et al. “Labeling images with a computer game.” (CHI 2004) [6] Ahn et al. “Improving the accessibility of the web with a computer game.” (CHI 2006)

  13. Outline • Web Studies • Providing Labels • WebInSight System • Evaluation • Future Work

  14. WebInSight System • Tasks • Coordinate multiple labeling sources • Insert alternative text into web pages • Add code to insert alternative text later • Features • Browsing speed preserved • Alternative text available when formulated • Immediate availability next time

  15. WebInSight Context Labeling Proxy OCR Labeling Human Labeling Database The Internet Blind User

  16. Outline • Web Studies • Providing Labels • WebInSight System • Evaluation • Future Work

  17. Evaluation • Measuring System Performance • WebInSight tested on web pages from web studies • Used Context and OCR Labelers • Labeled 43.2% of unlabeled, significant images • Sampled 2500 for manual evaluation • 94.1% were correct • Proper Precision/Recall Trade-off

  18. Evaluation: Demo

  19. Conclusion • Lack of alternative text is pervasive • WebInSight calculates alternative text • WebInSight inserts alternative text • High precision and moderate recall

  20. Future Work Users Content Producers • User Studies • What do users want? • How can we provide it? • Maintain experience. • User Studies • Designer motivation. • Tools for Web Design • People can always be better • Adapt user techniques Common Themes • Technology • Improved labeling • Bring closer to user • Move beyond images • More challenges • Content Structure • Dynamic Content • Web applications

  21. WebInSight http://webinsight.cs.washington.edu Thanks to: Luis von Ahn, Scott Rose, Steve Gribble and NSF.

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