Towards Detecting Dyslexia in Children's Handwriting Using Neural Networks

Dyslexia is a learning disability that hinders a person's ability to read. Dyslexia needs to be caught early, however, teachers are not trained to detect dyslexia and screening tests are used inconsistently. We propose (1) two new data sets of handwriting collected from children with and without dyslexia amounting to close to 500 handwriting samples, and (2) an automated early screening technique to be used in conjunction with current approaches, to accelerate the detection process. Preliminary results suggest our system out-performs teachers.

Speaker bio: Katie Spoon recently completed her B.S./M.S. in computer science from Indiana University with minors in math and statistics, and with research interests in anomaly detection, computer vision, data visualization, and applications of computer vision to health and education, like her senior thesis detecting dyslexia with neural networks. She worked at IBM Research in the summer of 2018 on neuromorphic computing, and will be returning there full-time. She hopes to potentially get a PhD and become a corporate research scientist.