Background

Text descriptions are considered to be one of the most important accessibility aids on the internet (Nielson, 2005). In WebAIM’s Screen Reader User Survey #4 (WebAIM 2012), only 35% of respondents thought web content accessibility had improved over the previous year. The survey also found that “images with missing or improper descriptions” were the 4th most problematic item for screen reader users (behind Flash content, CAPTCHA, and ambiguous links). However, several major issues prevent text descriptions from being widely used. First, there are billions of images on the internet.  For example, in 2010, Flickr hosted 5 billion photos increasing to 6 billion in 2011. In 2010, Facebook users uploaded over 3 billion photographs per month increasing to more than 100 billion photographs monthly in 2011. Instagram users uploaded 3,600 images per minute in 2011 (Pingdom, 2012). These numbers exemplify the enormity of having text descriptions available for all images. Second, useful text descriptions are not easy to write, and can be time consuming. One study found that it takes about 15 minutes for a novice to write a text description for a single image (Maggard, 2008). Third, recently conducted focus groups of blind and visually impaired individuals found sometimes contradictory preferences for the type and amount of information contained in a text description (Baumann & Smith, 2012).

Current Standards and Methods of Implementation 

There are several mechanisms for authors to provide text descriptions for images on a HTML webpage. The most widely known and implemented is the alt attribute on <img> tags which dates back to the first formal specification of HTML, HTML 2.0 (Berners-Lee & Connolly, 1995). However, it was included due to technical issues (e.g. the slow connections of the time), rather than for accessibility. HTML 4.0 made alt required for valid HTML, which helped increase awareness. Since screen readers default to vocalizing the text from the alt attribute (“alt text”) when an image with the attribute is encountered, best practices have developed to improve the experience of users. These best practices include setting blank alt text for non-content/decorative images, and keeping alt text short and focused.

In order to accommodate longer and more detailed descriptions, the longdesc attribute, which indicates a link to a long description of the image, was added in HTML 4.0 (W3C, 1997). Unfortunately, even though the HTML 4.0 specification was published in 1997, longdesc is still not widely known or implemented. In fact, an analysis in 2007 found that out of 1 billion images only 0.13% had a longdesc attribute (Pilgrim, 2007). Additionally, many images with the longdesc attribute did not implement it correctly. From the user side, WebAIM’s Screen Reader User Survey #3 found that over 20% responded “I don’t know” when asked about the usefulness of longdesc (WebAIM, 2011), implying they either did not know longdesc existed or had not experienced using it.

Due to the low adoption of longdesc the World Wide Web Consortium (W3C) proposed that it be removed from the HTML5 specification. There was a significant response from the web accessibility community, and a list was assembled of 182 real websites using longdesc (Carlson, n.d.). The W3C has not yet reached a final decision on longdesc in HTML5 (W3C, 2008). Additional methods of text description implementation have been made possible Accessible Rich Internet Applications (WAI-ARIA) standard (W3C, 2011), such as a hybrid aria-describedby and longdesc (Lembree, 2011).

It is important to note that, by default, web browsers do not inform sighted users of the existence of alt or longdesc attributes. Thus, sighted users often do not realize that even if tooltips are displayed when hovering over an image (which are specified by the web page author using the title attribute), the web page may not be accessible to screen reader users.

Another problem with both alt text and longdesc are the lack of standards for content. There is no set length of an alt text or longdesc, and it is up to the author to provide the information.

Survey of Alt text and longdesc on Top Websites

The home pages of the internet’s top 500 websites, as ranked by Alexa (Alexa, n.d.), were analyzed on January 8, 2013 for their implementation of alt and longdesc attributes. Out of the 20,138 images found, 4,924 (24.5%) had no alt attribute. Only 196 images (0.97%) had a longdesc attribute, but all were implemented incorrectly, containing URLs of images instead of URLs of webpages with text descriptions. Out of 2508 images with title text, 1980 (78.9%) had alt text which exactly matched the title text, and another 120 (4.8%) had title text but no alt attribute.

Current Methods of Writing text Descriptions

The Diagram Center has developed the Poet Image Description Tool (Diagram Center, n.d.), to add image descriptions to Daisy audio books, including a step by step process and basic guidelines, including language, context of the image, how the image is being used, and audience level.  

The R₂D₂ Center at UW-Milwaukee has created a three-tiered structure Equivalent Text Description (EqTD) protocol for writing text descriptions (R2D2 Center, n.d.), which include “brief”, “essential” and “detailed” descriptions. The brief description can be thought of as a title, with enough information to allow the reader to decide whether to move to the next level or skip it. The essential description includes the meaning of the picture, and what is its purpose within the context it is being used. The detailed description contains detailed visual descriptions of the image that could be used by a person who is blind to describe the image to a sighted person.

Both of these protocols are heavily dependent on the author, provide no specific length guidelines, and require significant training and practice for authors to become proficient in following the prescribed guidelines. Given the nearly incalculable number of images being uploaded by millions of users, it is unreasonable to think that enough authors can be trained using these techniques to even begin attacking the problem. Additionally, it is not known if these guidelines actually address the needs of the consumers.

End user viewpoints

Two focus groups were conducted to allow blind and individually impaired individuals to comment on what type of content they would like in a text description.  The individuals were presented with text descriptions and asked to comment on those specifically. Several themes were evident. All participants agreed on the desire and need for good text descriptions, but differed on the content. Participants commented that too much information was not good, but would rather have too much than none at all. Also, the participants stated that they would like to be able to choose the amount and level of information. Several individuals stated that they did not want to be told the meaning of the image, but would rather form their own interpretation, and thus would prefer more basic description. This was mitigated by the current and past level of vision. For individuals with developmental blindness, visually descriptive words are often meaningless, but they may give important information to someone who has an acquired visual impairment.  The context of the image was seen as vitally important. Participants stated that they preferred text descriptions only in cases in which the information was not redundant to the text.  Also, images text descriptions were considered essential for images such as maps, graphs, and math and science images, pictures of products for online shopping, and icons and on-screen buttons.

From the hypothetical alt text descriptions in Table 1 it is clear that inaccurate and incomplete descriptions of images can impose significant barriers to understanding. Figure 1 is also an example of an image which should have a long description (unless a table listing results by county is provided).

Conclusions

The number of images being uploaded to the internet is immense. Even the current, insufficient standards of alt text and longdesc are not being implemented. The current strategies for writing text descriptions include extensive training by the authors, and take time. Even then, the text descriptions produced may not meet the needs or wants of the consumers of text descriptions. Thus, the need for a comprehensive, multidisciplinary approach to writing and implementing text descriptions that becomes less dependent on human authors and more automated. Current research in computer vision (Gupta, Srinivasan, Shi, & Davis, 2009; Siddiquie & Gupta, 2010) and natural language generation (Yao, Yang, Lin, Mun Wai & Song-Chun, 2010; Demir, Carberry, & McCoy, 2011).make recognition of objects within a picture and their relationship to each other more feasible. Natural language generation allows for intelligent learning systems that, when paired with computer object recognition, could allow for at least detailed visual descriptions of an image with minimal human input. Computer scientists and web designers must work with accessibility experts and members of the blind and visually impaired communities to develop methods of allowing consumers, including those who use screen readers quick and easy access to multiple levels of content, depending on their desire at the time.  Accessibility experts must work with the blind and visually impaired communities to ensure that the content being provided is actually what is wanted.