Enhancing Textual Accessibility for Readers with Dyslexia through Transfer Learning

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.