Based Model for Detecting Dyslexia Using Handwritten Images

Abstract

This study aims to identify dyslexia in elementary school students through handwriting analysis. Dyslexia, which is often detected late, can affect students' academic abilities, especially in reading and writing. Given the importance of early detection, this study explores the use of handwriting as an indicator for the early diagnosis of dyslexia in elementary school-aged children. The methods used in this study included collecting data from writing assignments given to students, where the quality of handwriting was measured based on several variables: legibility, writing speed, letter reversal, and spacing between letters. The collected data were then analyzed using qualitative and quantitative approaches to compare the handwriting characteristics of dyslexic students with those of non-dyslexic students. The results of the analysis showed that students with dyslexia exhibited deficits in legibility and writing speed, and were more likely to reverse letters or be inconsistent in letter formation, in line with previous findings in the relevant literature (Van Heuverswyn et al., 2024; Patil et al., 2024). This study confirms that handwriting analysis can be an effective screening tool for detecting dyslexia at the elementary school level, and the results of this study are expected to contribute to the development of more effective early detection methods in elementary schools in Indonesia.