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Related Experiment Video

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Image-Based Recognition of Children's Handwritten Arabic Characters Using a Confidence-Weighted Stacking Ensemble.

Helala AlShehri¹

¹Computer and Information Technology Department, Jubail Industrial College, Jubail 35718, Saudi Arabia.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This summary is machine-generated.

This study introduces a novel stacking ensemble framework for recognizing children's Arabic handwriting, significantly improving accuracy and reliability. The method enhances automated educational assessment tools and intelligent tutoring systems.

Keywords:

Arabic character recognition child handwriting confidence thresholding convolutional neural networks deep learning ensemble learning machine learning

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Handwritten Arabic character recognition, especially for children, is complex due to writing variations and diacritics.
Deep learning models show promise but struggle with reliability in this domain.

Purpose of the Study:

To develop a robust stacking ensemble framework for sensor-acquired Arabic handwriting data.
To enhance prediction reliability using a dynamic confidence-thresholding mechanism.

Main Methods:

Integrated three Convolutional Neural Networks (ConvNeXtBase, DenseNet201, VGG16) using a fully connected meta-learner.
Implemented an optimized confidence threshold to filter uncertain predictions, maximizing the F1 score.
Evaluated the framework on the Hijja and Dhad benchmark datasets for children's Arabic handwriting.

Main Results:

Achieved state-of-the-art performance: 95.13% accuracy and 94.62% F1 score on Hijja.
Achieved 96.14% accuracy and 95.59% F1 score on Dhad.
Demonstrated over 3% accuracy improvement on Hijja compared to existing methods.

Conclusions:

Confidence-based stacking ensembles effectively enhance reliability in Arabic handwriting recognition.
The proposed framework shows strong potential for automated educational assessment and intelligent tutoring systems.