1. WebInSight: Making Web Images Accessible Jeff Bigham Richard Ladner Ryan Kaminsky Gordon Hempton Oscar Danielsson

2. Some statistics 10 million blind people in the U.S.  55,000 blind children  5 million blind people over 65 Computer and Internet Use  1 million use computers  < 200,000 have access to the Internet  < 100,000 use a computer regularly  32% of legally blind adults employed Source: American Foundation for the Blind Blindness Statistics http://www.afb.org/Section.asp?SectionID=15

3. Browsing the web while blind Blind users use screen readers Alternative text is substituted for images When no alternative text provided  nothing  filename (060315_banner_253x100.gif)  link address W3C accessibility standards  “Provide a text equivalent for every non-text element”  For images with purely visual purpose, a text equivalent is an empty string

6. Outline Web Studies WebInSight System Where Labels Come From Evaluation Future Work

7. Web Studies Images can be significant or insignificant Significant images need alternative text  alt, title, and longdesc HTML attributes Insignificant images need empty alternative text  (spacers, lines, wacky backgrounds, etc.) Significance from size, color and function

8. Web Studies: Groups CSE Traffic  1 week.  11,989,898 images.  40.8% significant 63.2% assigned alternative text Popular/Important Websites  500 High-Traffic International Sites  100 Top International Universities  158 Computer Science Departments  137 Federal Agencies  50 States plus District of Columbia

9. Study Results GroupSignificantInsignificant> 90%Number High-traffic39.627.421.832913 Computer Science 52.541.627.04233 Universities61.570.251.53910 U.S. Federal Agencies 74.866.655.95902 U.S. States82.577.152.92707

10. Result Graphs

11. Outline Web Studies WebInSight System Where Labels Come From Evaluation Future Work

12. WebInSight Add alternative text as a user browses Coordinate multiple labeling sources Avoid harming the user experience Maintain security and privacy

13. Database WebInSight Architecture The Internet Context Labeling OCR Labeling Human Labeling Transformation Proxy GET http://www.cs.washington.edu GET http://www.cs.washington.edu/ GET http://www.cs.washington.edu Login: _______ Pass: _______ Login: _login___ Pass: _pass___

15. WebInSight as a Proxy Transformation proxy  Inserts alternative text into webpages  Inserts AJAX hooks to allow later changes Advantages  Centralized control  Simple setup and administration Disadvantages  Potentially a bottleneck  Less control over user interface  Secure connections don’t benefit or are less secure

16. Outline Web Studies WebInSight System Where Labels Come From Evaluation Future Work

17. Providing Labels: Context Labeling Many important images are links  Linked page often describes image  Function much better than nothing

18. Providing Labels: OCR Labeling Original image not recognized (No Text) Find major colors Highlight major colors & try again

19. Providing Labels: OCR Labeling 2 ColorImageOCR Text (No Text) (PIC)t (No Text),,.,,,,.,.,,,,,.,,,n(PIC) (PIC) Register now!

20. Providing Labels: OCR Evaluation Tested 100 images containing text  The OCR correctly labeled 52  Our processing correctly labeled 65

21. Providing Labels: Human Labeling Humans are best labelers Luis von Ahn’s games get people to do it WebInSight sends images to such services

22. Outline Web Studies WebInSight System Where Labels Come From Evaluation Future Work

23. Evaluation Experiment  Run WebInSight on pages from Web Studies  43.2% of unlabelled sig. images labelled  Of these, 94.1% were correct

24. Evaluation: UCLA

25. Future Work User Studies  Does it help?  What do user’s want out of alt text?  When should WebInSight provide it? Refactoring alt text  Present alt text in the best way possible for users Tool for Webmasters  People will always be better but they need help

26. Demo