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Developmental dyslexia detection using machine learning techniques : A survey
Author(s) -
Shahriar Kaisar
Publication year - 2020
Publication title -
ict express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.733
H-Index - 22
ISSN - 2405-9595
DOI - 10.1016/j.icte.2020.05.006
Subject(s) - dyslexia , spelling , psychology , learning disability , reading (process) , developmental dyslexia , cognitive psychology , electroencephalography , learning to read , feeling , anger , developmental psychology , clinical psychology , linguistics , social psychology , neuroscience , philosophy
Developmental dyslexia is a learning disability that occurs mostly in children during their early childhood. Dyslexic children face difficulties while reading, spelling and writing words despite having average or above-average intelligence. As a consequence, dyslexic children often suffer from negative feelings, such as low self-esteem, frustration, and anger. Therefore, early detection of dyslexia is very important to support dyslexic children right from the start. Researchers have proposed a wide range of techniques to detect developmental dyslexia, which includes game-based techniques, reading and writing tests, facial image capture and analysis, eye tracking, Magnetic reasoning imaging (MRI) and Electroencephalography (EEG) scans. This survey paper critically analyzes recent contributions in detecting dyslexia using machine learning techniques and identify potential opportunities for future research.

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