Multilingual symbolic support for low levels of literacy on the Web

Abstract

Although literacy rates around the world have increased and there is an expectation that individuals who access web pages will be able to read their content, this is not always the case. The barriers that may be faced can be linked to the way the system is designed and content is written. There may be complex language or a layout that is dense, cluttered and lacks clear markers regarding the key points being made.

Many organizations have provided guidance for web developers and authors offering suitable ways to ensure those accessing a website or service will have a pleasurable experience. However, it appears that there are still websites hosting pages with dense text, convoluted instructions and little support for those with low levels of literacy. When considering poor reading skills, the cause may be due to many factors including a lack of education, sensory and /or intellectual impairments and specific difficulties such as dyslexia. This means that the vast majority of online content may be hard to understand for a significant proportion of the world’s population. Moreover, these individuals may also lack digital skills, with little realization that assistive technologies and the availability of supportive access strategies can be helpful in these situations.

This paper aims to introduce the idea of enhancing readability of web content by using artificial intelligence (AI) techniques, such as linked data, natural language processing and image recognition to make available a wide range of automatically mapped multilingual symbols that can be used to clarify text content. In the past only a few symbol sets have been mapped and it was not possible to consider their appropriateness for text to symbol translations in a wide range of languages and cultural settings.

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Identifiers

ORCID for E.A. Draffan: orcid.org/0000-0003-1590-7556

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