Beyond text analysis : image-based evaluation of health-related text readability using style features

Abstract

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' style to predict their readability score. The style features are extracted by analyzing only one page of the document as an image. 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. We provide an end-user program, VisualGrader, which provides a Graphical User Interface to the scoring model.