1. 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)

4. nav_svcs.gif

5. Outline Web Studies Providing Labels WebInSight System Evaluation Future Work

6. 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”> More than one color AND both dimensions > 10 pixels An associated action (clickable, etc.)

7. 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)

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

9. Study Results GroupSignificantPages > 90%PagesImages High-traffic39.6%21.8%50032913 Computer Science 52.5%27.0%1584233 Universities61.5%51.5%1003910 U.S. Federal Agencies 74.8%55.9%1375902 U.S. States82.5%52.9%512707 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.

10. Outline Web Studies Providing Labels WebInSight System Evaluation Future Work

11. Providing Labels: Context Labeling Many important images are links  Linked page often describes image  What happens if you click People of UW People …

12. [4] Jain et al. “Automatic text location in images and video frames.” (ICPR 1998) Providing Labels: OCR Labeling Improvement through Color Clustering [4] ColorNew ImageText Produced,,.,,,,n Register now! (Optical Character Recognition) Improves recognition 25% relative to base OCR!

13. Providing Labels: Human Labeling 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) [5] [6]

14. Outline Web Studies Providing Labels WebInSight System Evaluation Future Work

15. 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

16. The Interne t Proxy Context Labeling OCR Labeling Human Labeling Database Blind User

17. Outline Web Studies Providing Labels WebInSight System Evaluation Future Work

18. 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

19. Evaluation: Demo

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

21. Future Work User Studies  What do users want?  How can we provide it?  Maintain experience. UsersContent Producers 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

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