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Deep convolutional neural network for isolated Arabic handwritten character recognition: design, evaluation, and
1Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia. etattar@kau.edu.sa.
Scientific Reports
|November 27, 2025
Summary
A new deep Convolutional Neural Network (CNN) accurately recognizes handwritten Arabic letters, achieving 96.8% accuracy. This advanced model significantly outperforms traditional methods for Arabic handwriting recognition tasks.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Handwritten Arabic character recognition is challenging due to script complexity.
- Traditional methods struggle with cursive forms, positional variations, and diverse styles.
Purpose of the Study:
- To develop a deep Convolutional Neural Network (CNN) for classifying isolated handwritten Arabic letters.
- To evaluate the CNN's performance against classical machine learning techniques.
Main Methods:
- A tailored deep Convolutional Neural Network (CNN) architecture was proposed.
- A balanced dataset of 28 Arabic alphabet classes was used with preprocessing and augmentation.
- Performance was assessed using cross-validation, confusion matrix analysis, and statistical testing.
Main Results:
- The proposed CNN achieved a classification accuracy of 96.8%.
- This significantly outperformed Support Vector Machine (SVM) at 85.3% and K-Nearest Neighbors (KNN) at 82.1%.
- A paired t-test confirmed the statistical significance of the CNN's superiority (p < 0.01).
Conclusions:
- The developed CNN offers a highly accurate solution for isolated handwritten Arabic character recognition.
- The model shows potential for applications like document digitization and assistive technologies.
- The architecture is extensible to other cursive-based languages and future work includes connected script recognition.
