Deep Learning Approach to Automated Detection of Dyslexia-Dysgraphia

Pratheepan Yogarajah, Braj Bhushan

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Owing to the concern regarding lack of reliance on cognitive
profile-based diagnosis of dyslexia, we conducted an automated diagnosis exclusively based on the handwriting sample. Handwriting sample
of 54 children (36 males, 18 females) studying between classes 1st to
th identified with ‘strong evidence of risk’ on Dyslexia Screening TestJunior (DST-J) was collected. 14 Hindi words (i.e. 5 two letter words, 6
words with Matras (vowel signs), and 3 conjoined consonants (Sanyukt
Akshar)) were selected for this study on the basis of graded difficulty
level. These Hindi words share features such as matras, killer strokes
(halants), and Sanyukt Akshar. A total of 267 images, 164 from children
with dyslexia-dysgraphia and 103 from age-matched normal control, were
collected for this study. These images were resized to a fixed height of
113 pixels along with different width sizes depending on the image aspect
ratio. A random number of patches with size of 113 × 113 pixels were
generated from each image. A Convolutional Neural Network (CNN)
using Keras and Tensorflow was successful in automatically identifying
powerful features with average accuracy of (86.14 ± 1.02)%. The findings endorse deep learning approach in automated detection/diagnosis
of dyslexia-dysgraphia
Original languageEnglish
Publication statusAccepted/In press - 12 Nov 2020
Event The 25th IEEE International Conference on Pattern Recognition - Milan, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 25th


Conference The 25th IEEE International Conference on Pattern Recognition
Abbreviated titleICPR2020
Internet address


  • Deep Learning
  • Dyslexia
  • D


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