Accessible images in computer science journals
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Transcript of Accessible images in computer science journals
Bruno Splendiani, Mireia Ribera
Adaptabit Group – University of Barcelona
DSAI Conference 2013 - 13/11/2013
Introduction Our work Methodology Results Conclusions Further work
Bruno Splendiani (2013) – [email protected] 2
Accessibility scientific literature published by computer science journals
Computer science researchers play an active role in digital accessibility research
Bruno Splendiani (2013) – [email protected] 3
Many computer science subjects rely on visual resources such as graphs, diagrams and charts.
Visual content in computer science academic papers is a critical information source
Readers with visual impairments cannot access graphical information
Bruno Splendiani (2013) – [email protected] 4
Bruno Splendiani (2013) – [email protected] 5
Sources: [1 -4]
“If I can’t see the image I read its description”
Recommended by the Web Content Accessibility Guidelines
Incorporated to the publishing workflow guidelines of
Bruno Splendiani (2013) – [email protected] 6
Readers
Access to the content
Publishers
Advantages in article retrieval
Fulfil accessibility regulations
Comply with social responsibility
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Analysis Do actual practices of image submission fulfil
basic recommendations on accessibility?
Proposal What should be done for including accessibility
practices and principles in the submission process of academic journals?
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9Bruno Splendiani (2013) – [email protected]
Follow accessibility policy?
Submission policy and guidelines related to the visual content?
Follow international guidelines on image submission?
10 journals in computer science in JCR
10Bruno Splendiani (2013) – [email protected]
Are figures “readable”?
Image format Color contrast and coding Label font type and size Minimum resolution Minimum dimensions
Do figures have alternative text?
Source: [4]
1 paper for each journal in PDF version
11Bruno Splendiani (2013) – [email protected]
The practices do not fulfill basic recommendations on accessibility
Images lack alternative descriptions
Lack of consistency between image submission policies and their application
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The visual information of computer science articles is not accessible to people with visual disabilities
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14Bruno Splendiani (2013) – [email protected]
Common set of international guidelines for image submission
universal accessibility improved
Easier graphical authoring: shared guidelines
15Bruno Splendiani (2013) – [email protected]
Start from existing guidelines on image description
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Some depth work done in common charts (bar, line, pie...)
ButNO clear foundation for describing
other types of images
The image is….It shows….Panel a shows..Panel b shows..Legend shows…
17Bruno Splendiani (2013) – [email protected]
Currently computer science articles are not accessible to everybody
Accessibility of scientific papers and their figures should gain relevance in computer science research
Research in the field will improve image accessibility
Improve retrieval
Improve the efficiency of the submission process
18Bruno Splendiani (2013) – [email protected]
Domain - specific ScienceTechnologyEngineeringMathematics
Graphic type - specificIllustrationDiagramPhotograph………
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Adapted to the image role/function within the article
Support the article results?
Offer a visual example?
Show numerical data?
Emphasize special details?
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In the context of all image-related textual information
Source: [5]
21Bruno Splendiani (2013) – [email protected]
Linked to the publishing workflow
Supporting formats
Recommendations in submission system
Automatic image check (contrast, resolution, font size..)
Alt text check
1. Zargar, S.T., J. Joshi, y D. Tipper. «A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks». IEEE Communications Surveys Tutorials, Volume PP Issue 99 (2013):1 -24 .
2. Wu, Gin-Der, y Pang-Hsuan Huang. «A Vectorization-Optimization-Method-Based Type-2 Fuzzy Neural Network for Noisy Data Classification». IEEE Transactions on Fuzzy Systems 21, n.o 1 (2013): 1-15.
3. Toews, Matthew and William M. Wells III. «Efficient and robust model-to-image alignment using 3D scale-invariant features». Medical Image Analysis 17, n.o 3 :271-282.
4. Chen, Zhihu, Kwan-Yee K. Wong, Yasuyuki Matsushita, y Xiaolong Zhu. «Depth from Refraction Using a Transparent Medium with Unknown Pose and Refractive Index». Int. J. Comput. Vision 102, n.o 1-3 (2013): 3–17.
5. Splendiani, B., Ribera,M. « Do physicians make their articles readable for their blind or low-vision patients? An analysis of current image processing practices in biomedical journals from the point of view of accessibility» (under review)
23Bruno Splendiani (2013) – [email protected]