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المؤلفKunhoth, Jayakanth
المؤلفAl-Maadeed, Somaya
المؤلفKunhoth, Suchithra
المؤلفAkbari, Younes
المؤلفSaleh, Moutaz
تاريخ الإتاحة2024-10-13T09:22:02Z
تاريخ النشر2024-01-01
اسم المنشورInternational Journal on Document Analysis and Recognition
المعرّفhttp://dx.doi.org/10.1007/s10032-024-00464-z
الاقتباسKunhoth, J., Al-Maadeed, S., Kunhoth, S., Akbari, Y., & Saleh, M. (2024). Automated systems for diagnosis of dysgraphia in children: A survey and novel framework. International Journal on Document Analysis and Recognition (IJDAR), 1-29.‏
الرقم المعياري الدولي للكتاب14332833
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190448699&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/60073
الملخصLearning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعAutomated systems
Dysgraphia diagnosis
Handwriting disability
Machine learning
العنوانAutomated systems for diagnosis of dysgraphia in children: a survey and novel framework
النوعArticle
الصفحات1-29
dc.accessType Open Access


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