Arabic Offline Character Recognition Model Using Non-dominated Rank Sorting Genetic Algorithm

Darwish, Saad M.; Hassan, Osama Farouk; Elzoghaly, Khaled Osama

doi:10.1007/978-3-030-58669-0_20

Cited by 1 publication

(2 citation statements)

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“…On the other hand, the DCNN model without enhancements achieved an average accuracy of 93.67% and 94.17% for validation. The differences in results are shown in [15], where the enhanced DCNN model outperformed the hybrid CNN and BDLSTM Model by a small margin and achieved a recognition accuracy of 94.32%. In addition, the work by [14] proposed three neural networks pipeline model to recognise printed text images of the APTI dataset of words that contains over two-million-word samples, which uses data augmentation techniques and was able to score a recognition accuracy of 94.30%.…”

Section: A Dcnn Modelmentioning

confidence: 99%

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A Novel Hybrid DL Model for Printed Arabic Word Recognition based on GAN

Alwaqfi¹,

Mohamad²,

Al-Taani³

et al. 2023

IJACSA

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The recognition of printed Arabic words remains an open area for research since Arabic is among the most complex languages. Prior research has shown that few efforts have been made to develop models of accurate Arabic recognition, as most of these models have faced the increasing complexity of the performance and lack of benchmark Arabic datasets. Meanwhile, Deep learning models, such as Convolutional Neural Networks (CNNs), have been shown to be beneficial in reducing the error rate and enhancing accuracy in Arabic character recognition systems. The reliability of these models increases with the depth of layers. Still, the essential condition for more layers is an extensive amount of data. Since CNN generates features by analysing large amounts of data, its performance is directly proportional to the volume of data, as DL models are considered data-hungry algorithms. Nevertheless, this technique suffers from poor generalisation ability and overfitting issues, which affect the Arabic recognition models' accuracy. These issues are due to the limited availability of Arabic databases in terms of accessibility and size, which led to a central problem facing the Arabic language nowadays. Therefore, the Arabic character recognition models still have gaps that need to be bridged. The Deep Learning techniques are also to be improved to increase the accuracy by manipulating the strength of technique in a neural network for handling the lack of datasets and the generalisation ability of the neural network in model building. To solve these problems, this study proposes a hybrid model for Arabic word recognition by adapting a deep convolutional neural network (DCNN) to work as a classifier based on a generative adversarial network (GAN) work as a data augmentation technique to develop a robust hybrid model for improving the accuracy and generalisation ability. Each proposed model is separately evaluated and compared with other state-ofthe-art models. These models are tested on the Arabic printed text image dataset (APTI). The proposed hybrid deep learning model shows excellent performance regarding the accuracy, with a score of 99.76% compared to 94.81% for the proposed DCNN model on the APTI dataset. The proposed model indicates highly competitive performance and enhanced accuracy compared to the existing state-of-the-art Arabic printed word recognition models. The results demonstrate that the generalisation of networks and the handling of overfitting have also improved. This study output is comparable to other competitive models and contributes an enhanced Arabic recognition model to the body of knowledge.

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Section: A Dcnn Modelmentioning

confidence: 99%

“…These challenges are attributed to the fact that most Arabic recognition research continues to face the increasing complexity of the system's performance, where few efforts have been made to enhance models of recognising Arabic printed words. This depicts that there is still vast room for enhancements [15] [16].…”

Section: Introduction Imentioning

confidence: 98%