Tag: web accessibility

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Tenth Global Accessibility Awareness Day (GAAD) May 20, 2021

Over the last few years there has been a general move towards seeing how AI can help individuals involved with digital accessibility overcome some of the barriers faced by those with disabilities.  The use of machine learning can also provide access via assistive technologies that have been improved to such an extent that they are needing less and less human intervention.  Examples include automatic captioning on videos such as those presented on YouTube and speech recognition.

The question is whether we have really moved on from Deque’s 2018 “Five Ways in Which Artificial Intelligence Changes the Face of Web Accessibility”.

These included: 

  • Automated image recognition,
  • Automated facial recognition,
  • Automated lip-reading recognition,
  • Automated text summarization,
  • Real-time, automated translations.

Visiting the GAAD events page  is often a good way to find out as many companies and organisations world wide share what they have achieved over the year, such as Google with its Machine Learning for Accessibility where they discuss Voice Access, Lookout, and Live Transcribe along with Sound Notifications for Android on May 19, 8:15 PM and Microsoft with its AI powered 365 event and others also listed on the Access 2 Accessibility site. 

There is an AI for Accessibility Hackathon (Virtual) on May 24th – June 29th 9-10am BST (Beirut, Lebanon) run by the ABLE CLUB American University Of Beirut.  This competition is aimed at rallying talents and fostering the regional development of the innovative entrepreneurship community related to artificial intelligence while also increasing social inclusiveness.

AccessiBe.com uses machine learning and computer vision technologies for image recognition and OCR as it scans web pages for accessibility issues, just as our Group Design Project team used similar technologies on Web2Access to highlight alt tags that were possibly a poor representation of an image on a website and where overlaps occurred when zoom was used as well as a visualisation of a site on a mobile phone if it failed WCAG guidelines.  

However, still to come is Apple’s use of AI for screen recognition on iOS 14, where it “uses on-device intelligence to recognize elements on your screen to improve VoiceOver support for app and web experiences” such as detecting and identifying “important sounds such as alarms, and alerts you to them using notifications.”

So let’s all celebrate the improvements in digital accessibility that AI can bring, whilst making sure that one day there will be no need to have an AccessiBe YouTube video about “why web accessibility matters.”  It will just be something we can take for granted!  Equal Access for All. 

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AI and Inclusion projects related to Web Accessibility and AAC support.

Over the last few months we have been concentrating on projects related to automated web accessibility checks and the automatic  linking and categorisation of open licenced and freely available Augmentative and Alternative Communication symbol sets for those with complex communication needs.

As has been mentioned we presented these projects at a workshop in the Alan Turing Institute in November and work has been ongoing. It is hoped that the results will be shared by the end of March 2020.

Automating Web Accessibility Checks

Recent regulations and UK laws recognise the W3C Web Content Accessibility Guidelines (WCAG) as a method of ensuring compliance, but testing can be laborious and those checkers that automate the process need to be able to find where more errors are occurring.  This has led to the development of an accessibility checker that carries out well-known automated checks, but also includes image recognition to make it possible to see if the alternative text tags for images are appropriate. A second AI related check involves a new WCAG 2.1 Success Criteria 2.4.4 Link Purpose (In Context).  This is where “the purpose of each link can be determined from the link text alone or from the link text together with its programmatically determined link context, except where the purpose of the link would be ambiguous to users in general”.[1] 

A Natural Language Processing (NLP) model is used to check whether the text in the aria-label attribute within the target hyperlink object matches the content in the target URL. Based on the matching result, it is possible to determine whether the target web page or website fit the link purpose criteria. Despite previous research in this area, the task is proving challenging with two different experiments being worked on. One experiment has been designed to use some existing NLP models (e.g. GloVe), while another one is investigating the training of data with human input. The results will be published in an academic paper and at a conference.

AAC symbol classification to aid searches.

Global Symbols with a Cboard user

The team have also investigated issues for those supporting Augmentative and Alternative Communication (AAC) users who may have severe communication difficulties and make use of symbols and pictures on speech generating devices. A multilingual symbol repository for families, carers and professionals has been created to link different freely available symbol sets.  The symbol sets can be used to create communication charts for the AAC user but this takes time and finding appropriate cultural symbols is not always easy.  A system has been developed that automatically links and categorises symbols across symbol sets related to their parts of speech, topic and language using a combination of linked data, natural language processing and image recognition.  The latter is not always successful in isolation as symbols lack context and concepts are not necessarily concrete such as an image for ‘anxious’, so further work is required to enhance the system.  The Global Symbols AAC symbol repository will be making use of these features on their BoardBuilder for making symbol charts by the end of March 2020.

This project is exploring some existing Convolutional Neural Network (CNN, or ConvNet) models to help classify, categorise and integrate AAC symbols. Experiments have already been undertaken to produce a baseline by simply using the image matrix similarity. Due to the nature of AAC symbols, some of these similar symbols are representing different concepts, but some different symbols are representing the same concept across different symbols sets. The training data set has mapped symbol images labels and NLP models have been used to map the labels into the same concept across different symbols. This will help those supporting ACC users offer much wider symbol choices suitable for different cultures and languages. The Global Symbols API for searching open licence and freely available AAC symbols is already being used in the Cboard application for AAC users

[1] https://www.w3.org/WAI/WCAG21/Understanding/link-purpose-in-context.html