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The research aims to develop an AI-based model to classify the severity of dysgraphia from handwriting data.

January 23, 2026IET Image ProcessingOpen Access

H2FCD: Fusion of Offline Handwritten Image Features and Histogram Features for Multiclass Classification of Dysgraphia Severity

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The research aims to develop an AI-based model to classify the severity of dysgraphia from handwriting data.
Fusion of image features and histogram features using a novel model called H2FCD.
Utilization of a convolutional neural network with ResNet-18 architecture.
Testing classification accuracy using three machine learning models, including support vector machine.
Achieved 99.26% accuracy using the support vector machine in cross-validation.
Successfully classified multiple dysgraphia severity levels.
Proposed a new deep feature extractor layer structure for improved analysis.

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Research demonstrates high accuracy in classifying dysgraphia severity using advanced AI techniques, suggesting efficient assessment methods.

Ramlan et al. (2026) studied this question.

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Research demonstrates high accuracy in classifying dysgraphia severity using advanced AI techniques, suggesting efficient assessment methods.