Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification

Raidah Salim Khudeyer, Noor Mohammed Almoosawi


The recognition or classification of Arabic handwritten characters is extremely crucial in many applications and, at the same time, one of the biggest challenges that machine learning faces. The emergence of deep learning, particularly Convolutional Neural Networks (CNN), is considered a suitable technique to face these challenges. In this research paper, an investigation model is proposed to make recognition for Arabic handwriting utilizing one of CNN architectures: ResNet50 architecture, after replacing the last layer with one of two types of machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to reduce training time and increase overall accuracy.

     Our experimental work was performed on three data sets: Arabic Handwritten Character Dataset (AHCD), Alexa Isolated Alphabet Dataset (AIA9K), and Hijja Dataset. Experimental results show that combining ResNet50 with random forest produces more accurate and consistent results than the ResNet50 model produces by itself.

     Finally, the comparison with the other methods across all data sets demonstrates the robustness and effectiveness of the combination of random forest with the ResNet50 approach. Where the modified ResNet50 architecture has achieved a rate of 92.37%, 98.39%, and 91.64%, while the combination architecture has achieved 95%, 99%, and 92.4% for AIA9K, AHCD, and Hijja datasets, respectively.

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