richard e. ladner and jeffrey p. bigham work with ryan kaminsky, gordon hempton, oscar danielsson...

Richard E. Ladner and Jeffrey P. Bigham

Work with Ryan Kaminsky, Gordon Hempton, Oscar DanielssonUniversity of Washington

Computer Science & Engineering

and everything else?

2

Accessibility Affects

People who are blind

People with visual impairments

People who are Deaf or hard of hearing

People with learning disabilities

People who are physically impaired

Web Accessibility Overview

3

Accessibility Affects (cont.)

People who use cell phones

People who use text browsers

Information extraction


4

Standards for Developers

W3C Web Content Accessibility Guidelines

Section 508 of the U.S. Rehabilitation Act

Americans with Diabilities Act (ADA)


5

Accessible Browsing Screen readers, refreshable Braille

displays

Consider Linear Display Separate presentation from meaning No vision or mouse required Visual content requires an alternative


6

Images Images cannot be read directly

W3C accessibility standard “Provide a text equivalent for every non-text

element”

What if no alternative text? Nothing Filename (060315_banner_253x100.gif) Link address (www.cs.washington.edu or /subdir/)


8

/olc/pub/YALE/oldintro/oldintro.cgi

Update Address

9

Cornell CS Webpage

10

Part II: Accessible Images

Web Studies

Providing Labels

WebInSight System

Evaluation

Developers

Making Images Accessible

11

Web Studies: All Images != Significant images need alternative

textalt, title, and longdesc HTML attributes

Insignificant images need empty alt text Decorative or structural

<img src=“graph.gif” alt=“annual growth: 1982 to 2004” title=“Annual Growth” longdesc=“growth_descrip.txt”>

<img src=“images/spacer.gif” width=“1” height=“1”><img src=“images/spacer.gif” width=“1” height=“1” alt=“”>


12

Image Significance

More than one color and both dimensions > 10 pixels

An associated action (clickable, etc.)


13

Web Studies Previous studies

All images: 27.9%[1], 47.7%[2], and 49.4%[2]

Significant images: 76.9%[3]

Concerns Variation Consideration of Image Significance and

Popularity[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)


14

Web Site StudyGroup

Significant

Pages > 90%

Pages Images

High-traffic 39.6% 21.8% 500 32913

Computer Science

52.5% 27.0% 158 4233

Universities 61.5% 51.5% 100 3910

U.S. Federal Agencies

74.8% 55.9% 137 5902

U.S. States 82.5% 52.9% 51 2707

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.


15

University of Washington CSE Department Traffic

Web Traffic Study

Significant images without alternative text.

Significant images withalternative text.

~1 week 11,989,898 images including duplicates 40.8% significant 63.2% alt text


16


Web Studies

Providing Labels

WebInSight System

Evaluation

Developers

17

<a href=“p234.htm”><img src=“p523.gif”></a><a href=“p234.htm”><img src=“p523.gif” alt=“People of UW”></a>

Providing Labels: Context Labeling Many important images are links

Linked page often describes image What happens if you click

<html><head><title>People of UW</title><body><h1>People</h1>…</body></html>


18[4] Jain et al. “Automatic text location in images and video frames.” (ICPR 1998)

Providing Labels: OCR Labeling

Improvement through Color Clustering[4]

ColorNew Image

Text Produced

,, ., ,,,n

Register now!

(Optical Character Recognition)

Improves recognition 25% relative to base OCR!


19

Providing Labels: Human Labeling

Humans are best Recent games compel accurate labeling WebInSight database has only 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]


21


Web Studies

Providing Labels

WebInSight System

Evaluation

Developers


22

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


23

The Internet

The Internet

Proxy

Context Labeling

OCR Labeling

Human Labeling

Database

Blind User


24

The Internet

The Internet

Extension Context Labeling

OCR Labeling

Human Labeling

Database

Blind User

Labeling Service


25

Concerns

Accuracy

Distribution of Tasks – who does what?

Authorization – who can use the system?

Privacy

Copyright


26

Part II: Accessible Images Web Studies

Providing Labels

WebInSight System

Evaluation

Developers

27

Evaluation

Measuring System Performance WebInSight tested on web pages from web site

study 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


28


29

Part II: Accessible Images Web Studies

Providing Labels

WebInSight System

Evaluation

Developers

30

Developers: Prior Work

A-Prompt U of Toronto as part of W3C initiative, 1999 Registry for alternative text Provides suggestions using heuristics on

filenames ALTifier

Proxy-based system Used filename/URL as alt text


31

WebInSight Developer Video

32

Conclusion

Lack of alternative text is pervasive

WebInSight formulates & inserts alt. text

Appropriate precision/recall tradeoff

Users and developers can use same technology


33

Part III: Future Research

Support Web Users and Developers

Automation and Suggestions

Independence

Sharing and Collaboration

Future Research

34

Understanding our users

Blind web users Remote observation with proxy server User diaries

Web developers Focus groups Surveys

Future Research

35

Technical Challenges

Relaying Content Structure tables, div, columns

Dynamic Content DHTML, mouse overs

Rich Internet Applications/Web Applications e-mail, word processing, spreadsheets

Requires new ways of reading the web

Future Research

37

Scripting Accessibility

Greasemonkey reshapes the web Accessmonkey facilitates accessibility

Getting technology to people Multiple platforms and implementations A conduit for collaboration Web users and developers share

technology

Future Research

38

Independence

Automation means independence

Helping users create scripts

Helping users share scripts

Future Research

39

Part IV: Related Projects

Related Projects

40

Graphic Translation <LocationInformation><NumLabels>16</NumLabels><Resolution>100.000000</Resolution><ScaleX>1.923077</ScaleX><ScaleY>1.953125</ScaleY>-

<Label><x1>121</x1><y1>45</y1><x2>140</x2><y2>69</y2><Alignment>0</Alignment><Angle>3.141593</Angle></Label>

preprocesstext extract

cleanimage

originalscannedimage

puregraphic

textimage

locationfile

Tactile Graphics

41

Graphic Translation<LocationInformation>

<NumLabels>16</NumLabels><Resolution>100.000000</Resolution><ScaleX>1.923077</ScaleX><ScaleY>1.953125</ScaleY>-

<Label><x1>121</x1><y1>45</y1><x2>140</x2><y2>69</y2><Alignment>0</Alignment><Angle>3.141593</Angle></Label>

puregraphic

textimage

locationfile

y(0,20)x=1515105Ox510152020x+y=20(15,0)(15,5)

y(#0,#20)x.k#15#15#10#5Ox#5#10#15#20#20x+y.k#20(#15,#0)(#15,#5)

text Braille

Tactile Graphics

42

Challenges: Limited network bandwidth Limited processing power on cell phones

MobileASL Project ASL communication using video cell phones

over current U.S. cell phone network

43

WebInSight

http://webinsight.cs.washington.edu

Thanks to: Luis von Ahn, Scott Rose, Steve Gribble and NSF.

richard e. ladner and jeffrey p. bigham work with ryan kaminsky, gordon hempton, oscar danielsson...

Documents