GEAC: Generating and Evaluating Handwritten Arabic Characters Using Generative Adversarial Networks

Alkhodidi, Tahani; Aljoudi, Lamia; Fallatah, Aisha; Bashy, Aya; Ali, Nahed M.; Alqahtani, Norah; Almajnooni, Nujood; Allhabi, Ahad; Albarakati, Telawa; Alafif, Tarik; Sasi, Sreela

doi:10.1109/iccike51210.2021.9410746

Cited by 3 publications

(1 citation statement)

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“…Consequently, different GANs generate various outputs. These types of GANs are used by [32] to generate different Arabic handwritten characters. The researchers split the proposed approach into two parts, one for generating an image and the other for evaluating the quality of the generated images by assessing how realistic and fake the generated images are.…”

Section: Related Work IImentioning

confidence: 99%

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: Related Work IImentioning

confidence: 99%

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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GEAD: generating and evaluating handwritten Eastern Arabic digits using generative adversarial networks

Alafif

Alharbi

Almajnooni

et al. 2021

Int. j. inf. tecnol.

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Unveiling New Artistic Dimensions in Calligraphic Arabic Script with Generative Adversarial Networks

Sobhan,

Pasquier,

Tindale

2024

Proc. ACM Comput. Graph. Interact. Tech.

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We present an artistic exploration into calligraphic Arabic script, focusing on the nastaliq style predominant in Iran, by harnessing the affordances of Generative Adversarial Networks (GANs). Recognizing the unique challenges posed by Arabic script's cursive nature and its inadequate representation by conventional tools, our work seeks to bridge the gap between traditional calligraphy and novel technological capabilities. Two custom datasets are introduced, Nas4-60k and Nas4-60k-aug, designed to train our generative networks in producing calligraphic Arabic. Utilizing the StyleGAN2-ada architecture, our approach successfully generates stylistically coherent and high-quality calligraphic samples. These samples exhibit meaningful feature extraction and generalization of calligraphic features, extending beyond the training sets. Furthermore, our system reveals a continuous spectrum of calligraphic features through latent space interpolations, leading to the creation of dynamic, innovative artworks that blend traditional and contemporary elements of Arabic calligraphy. Drawing inspiration from the compositional form of siyah-mashq, our work culminates in multiple publicly presented artworks that exhibit a new mode of creative expression and highlight the potential of GANs in unveiling new artistic dimensions in calligraphic Arabic script.

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GEAC: Generating and Evaluating Handwritten Arabic Characters Using Generative Adversarial Networks

Cited by 3 publications

References 1 publication

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

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

GEAD: generating and evaluating handwritten Eastern Arabic digits using generative adversarial networks

Unveiling New Artistic Dimensions in Calligraphic Arabic Script with Generative Adversarial Networks

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