A Comparative Review of Lightweight Machine Learning Approaches for Handwriting-Based Dyslexia Detection
Krispy Tyagi · International Journal for Research in Applied Science and Engineering Technology · 2025
Dyslexia, a neurodevelopmental disorder affecting reading and writing skills, requires early detection to mitigate longterm educational impacts. Traditional diagnostic methods are time-consuming and subjective, prompting the adoption of automated handwriting analysis. This paper reviews lightweight machine learning (ML) approaches for dyslexia detection through handwriting, emphasizing computational efficiency, real-time applicability, and cross-linguistic adaptability. By evaluating techniques such as MobileNetV2, SSD Lite, Support Vector Machines (SVM), and Random Forests across languages like English, Hindi, Arabic, and Chinese, we highlight trade-offs between accuracy, efficiency, and script-specific challenges. Our analysis reveals that lightweight models achieve competitive performance while addressing issues like accessibility, making them valuable for use in resource-constrained environments like classrooms