Automated Detection of Dyslexia Symptom Based on Handwriting Image for Primary School Children

• Main Authors: Balfagih Z. (Author), Halabi W. (Author), Ibrahimi T. (Author), Isa, I.S (Author), Lytras M. (Author), Rambo K. (Author), Ramlan, S.A (Author), Sarirete A. (Author), Sulaiman, S.N (Author), Syazwani Rahimi, W.N (Author), Uddin M. (Author), Visvizi A. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2019
• Subjects: artificial neural network; Automation; Biological neural systems; Classification accuracy; dyslexia handwriting; Dyslexia symptom; Image processing; Information processing systems; Neural networks; Optical character recognition; Optical character recognition (OCR); Optical data processing; pattern recognition method; Pattern recognition method; Pattern recognition systems; Pattern recognition techniques
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 18770509 (ISSN)
• DOI: 10.1016/j.procs.2019.12.127

This paper presents an automated detection system to identify the present of dyslexia symptoms in primary school children based on their handwriting images. The proposed automated detection system is developed by using pattern recognition technique. Based on their handwriting images, the pattern recognition system will detect and extract the features of the written characters using Optical Character Recognition (OCR). By comparing the automated correct detection and manually calculation, the accuracy of the classification is obtained of 73.33%. An artificial neural network (ANN) is an information processing system that is inspired by the ability of biological neural systems to process information. The performance of the proposed system is evaluated based on the accuracy of the ANN which is also used to classify the levels of dyslexia risks namely as low risk (LR) and risk (R). The best numbers were selected by maximum value of the classification accuracy on the test value is 0.7083 which the value of hidden nodes use in ANN is 4. The performance of the classification accuracy is immediate. For future works, need more samples and add more features in image processing. The proposed automated system for detecting of early dyslexia symptoms is able to overcome several drawbacks of current screening methods for the dyslexic children. © 2019 The Authors. Published by Elsevier B.V.