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Enhancing Web Image Accessibility for Blind Users

This paper explores strategies to improve the accessibility of web images for visually impaired users. It emphasizes the importance of alternative text as mandated by W3C accessibility standards, detailing how significant images should have descriptive labels while insignificant images require empty alt text. The discussion includes findings from web studies analyzing nearly 12 million images, highlighting gaps in current practices and evaluating the WebInSight system, which aids in labeling images through human and automated methods. Future work aims to refine user interaction and expand accessibility.

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Enhancing Web Image Accessibility for Blind Users

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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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