Dyslexia recognition by handwriting using Convolutional Neural Networks (#1346)

Authors

Nuñez-Medrano, Yuri

Espinoza-Vicuña, Carlos Enmanuel

Abstract

This project focuses on using technological tools to describe and classify handwriting behaviors in children. This in order to help a proper diagnosis of dyslexia. It was necessary to collect all the necessary information on Developmental Dyslexia. Thus studying the origin, characteristics, diagnosis, treatments, etc. There are several tests to carry out a diagnosis. These are focused on the phonetic behavior of the voice during a reading. In addition, reading comprehension tests, among others. It has been decided to develop a mobile application, which performs reading-writing tests in participants with and without dyslexia. Thus, finding a large number of patterns that can be studied, mainly related to Dysgraphia, Dysortography and Dyscalculia. After collecting the images from the tests carried out, the second part is to find an artificial intelligence model that allows them to be classified. 2 models were used: convolutional model 1 and the Unet model. These models gave promising results. Finally, the need to continue collecting more images of reading-writing tests in patients with dyslexia was observed.