Early Detection and Severity Assessment of Dysgraphia in Sinhala-Speaking Children Using a Multi-Modal Machine Learning Approach

Early Detection and Severity Assessment of Dysgraphia in Sinhala-Speaking Children Using a Multi-Modal Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Detection and Severity Assessment of Dysgraphia in Sinhala-Speaking Children Using a Multi-Modal Machine Learning Approach Sandushi Weraduwa, PPG Dinesh Asanka, Thilini V. Mahanama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6588752/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract Dysgraphia is a neurological learning disability that impairs handwriting, spelling, and the ability to express thoughts in written form. Despite its significant impact on children's academic performance and psychological well-being, early detection remains a challenge particularly in linguistically and culturally unique settings like Sri Lanka. Existing diagnostic tools are largely developed for English-speaking populations and fail to address Sinhala language-specific characteristics. To bridge this gap, this research introduces the first multi-modal sequential machine learning framework in Sri Lanka for the early detection and severity assessment of Dysgraphia among Sinhala-speaking primary school children. The proposed approach integrates two key components: a Convolutional Neural Network (CNN) utilizing VGG16 for handwriting image analysis, and a Gradient Boosting classifier for interpreting cognitive, behavioral, and personal data. The dataset comprises 373 digitized handwriting samples from 84 children, collected from local schools and a pediatric hospital, using psychological assessments from Indian contexts adapted to Sinhala language materials. Handwriting samples were preprocessed through binarization and color inversion for model input. The handwriting-based Dysgraphia detection model achieved an accuracy of 96%, while the severity assessment model reached 87%. This pioneering multi-modal sequential approach significantly enhances diagnostic precision and reliability, enabling earlier interventions and individualized academic support. The study represents a vital step toward inclusive education in Sri Lanka, leveraging advanced AI techniques to meet a critical need in local learning disability diagnostics. Artificial Intelligence and Machine Learning Special Education Convolutional Neural Network Dysgraphia Multi-modal Techniques Sinhala Language VGG16 Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFiles.docx Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6588752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472736702,"identity":"80300100-5bb7-4915-8981-d6729b9ed535","order_by":0,"name":"Sandushi Weraduwa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-1937-0179","institution":"Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka","correspondingAuthor":true,"prefix":"","firstName":"Sandushi","middleName":"","lastName":"Weraduwa","suffix":""},{"id":472736703,"identity":"b5ec8d08-6e27-4a80-99b2-b5578dde4fce","order_by":1,"name":"PPG Dinesh Asanka","email":"","orcid":"https://orcid.org/0000-0002-3433-5533","institution":"Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka","correspondingAuthor":false,"prefix":"","firstName":"PPG","middleName":"Dinesh","lastName":"Asanka","suffix":""},{"id":472736704,"identity":"bc25229f-24d2-4a78-a064-9a14fa1c8a11","order_by":2,"name":"Thilini V. 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