CNN-Based Dysgraphia Classification: A Comparative Study on Word and Paragraph Level Handwriting Analysis

Authors

  • Elizabeth Vinitha P V , Pramod Pavithran Author

DOI:

https://doi.org/10.48047/32kfj634

Keywords:

Dysgraphia classification, CNN handwriting analysis, Deep learning for learning disorders, MobileNetV2, ResNet50, EfficientNetB7, Dysgraphia detection.

Abstract

Aims: To evaluate the effectiveness of ResNet50, MobileNetV2, and EfficientNetB7—for classifying dysgraphia-affected handwriting at the word and paragraph levels using a custom Malayalam dataset. Study design:  An experimental study using deep learning models for dysgraphia classification. 

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Published

05.06.2025

Issue

Vol. 15 No. 6 (2025): volume 15 issue 6(2025)

Section

Articles

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How to Cite

CNN-Based Dysgraphia Classification: A Comparative Study on Word and Paragraph Level Handwriting Analysis. (2025). International Journal of Information and Electronics Engineering, 15(6), 77-85. https://doi.org/10.48047/32kfj634