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Abstract

Due to its complexity, the Arabic language and its extensions build a fertile field of research in the field of artificial intelligence in general and optical character recognition (OCR) specifically. There are several languages that use the Arabic alphabet in their manuscripts. These languages innovated new letters to pronounce sounds not found in the Arabic language. These letters are called 'Arabic-derived letters'. To enrich the Arabic language, we can use these letters to know the true pronunciation of intrusive words in the Arabic language. This article deals with the Arabic-derived letters (ADL) dataset. It is a new dataset that consists of 55440 scanned images of papers written by 30 participants of different ages, with a data augmentation technique to increase the number of images. This study aims to evaluate and compare the effectiveness of different convolutional neural network architectures for ADL recognition, focusing on accuracy, robustness, and generalization capability. Three architectures were implemented: LeNet, a simplified ResNet model with residual blocks, and a deep VGG-Like network. Training was limited to 40 epochs with early stopping after 5 epochs without improvement. Experimental results show that the VGG-Like model achieves the best performance with 99.61% accuracy in validation, closely followed by ResNet with an accuracy of 98.98%. In contrast, LeNet performs less efficiently by 96.43%. These results clearly demonstrate that modern and deep architectures provide better accuracy and robustness for the classification of handwritten characters.

Keywords

Arabic handwritten, Convolutional neural network, Derived Arabic letters, Optical character recognition, VGG-Like, ResNet, LeNet

Subject Area

Computer Science

Article Type

Article

First Page

1084

Last Page

1099

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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