Research

Depth, Thinking & Innovation

Final Year Research Project

Neural Network-Based System for Early Detection & Intervention of Dysgraphia in Children

Bridging the gap in early detection and intervention for writing disabilities

The Problem

Existing models primarily focus on alphabetic languages and datasets from Western contexts, leaving a significant research gap in numeric dysgraphia and regional language adaptation.

This study aims to address these gaps by developing a neural network-based dysgraphia detection and intervention platform for Sri Lankan children. The system combines CNN-based handwriting analysis with interactive, gamified interventions designed to enhance fine-motor and cognitive skills.

The Solution

CNN-based Handwriting Analysis — Deep learning models to detect writing patterns
ML/DL Comparison — Benchmarking traditional vs. neural approaches
AI Chatbot + 3D Avatar — Interactive guidance for parents & children
Intervention Activities — Gamified exercises to improve writing skills