Enhancing Textual Accessibility for Readers with Dyslexia through Transfer Learning
Article No.: 63, Pages 1 - 5
Abstract
This paper explores automated modification of text to make it more accessible for people with dyslexia, a reading disorder affecting a significant percentage of the global population. The modifications are both in terms of changing the appearance of text and simplification of the words, grammar, and length of textual material. For simplification of text, we built a dataset with original and dyslexia-friendly text verified by human readers that it improve their reading experience by 27% on average. Then we developed a pipeline to generate dyslexia-friendly text automatically using transfer learning. The model learns styles appropriate for dyslexic users and generates dyslexia-friendly text from arbitrary textual data, which is easier for people with dyslexia to read and interpret.
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Index Terms
- Enhancing Textual Accessibility for Readers with Dyslexia through Transfer Learning
Index terms have been assigned to the content through auto-classification.
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October 2023
1163 pages
ISBN:9798400702204
DOI:10.1145/3597638
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Published: 22 October 2023
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ASSETS '23
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ASSETS '23: The 25th International ACM SIGACCESS Conference on Computers and Accessibility
October 22 - 25, 2023
NY, New York, USA
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ASSETS '23 Paper Acceptance Rate 55 of 182 submissions, 30%;
Overall Acceptance Rate 436 of 1,556 submissions, 28%
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