Using Image Processing to Assess the Readability of Health Documents PROCEEDINGS
Freddy Bafuka, Dorothy Curtis, William Long, MIT, United States ; Qing Zeng-Treitler, University of Utah, United States
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Orlando, Florida, USA ISBN 978-1-880094-83-9 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
Many studies have shown that the readability of health documents presented to consumers does not match their reading levels. An accurate assessment of the readability of health-related texts is an important step in providing material that match readers' literacy. Current readability measurements depend heavily on text analysis (NLP), but neglect style (text layout). In this study, we show that style properties are important predictors of documents’ readability. In particular, we build an automated computer program that uses documents' styles to predict their readability score. The scores produced by our system were tested against scores given by human experts. Our tool shows stronger correlation to experts’ scores than the Flesch-Kincaid readability grading method.
Bafuka, F., Curtis, D., Long, W. & Zeng-Treitler, Q. (2010). Using Image Processing to Assess the Readability of Health Documents. In J. Sanchez & K. Zhang (Eds.), Proceedings of E-Learn 2010--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 17-24). Orlando, Florida, USA: Association for the Advancement of Computing in Education (AACE). Retrieved October 21, 2018 from https://www.learntechlib.org/primary/p/35500/.
© 2010 Association for the Advancement of Computing in Education (AACE)
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