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

Abstract

Generative Adversarial Network (GAN) has made a breakthrough and great success in many research areas in computer vision. Different GANs generate different outputs. In this research work, we apply different GANs to generate handwritten Arabic characters. A basic GAN, Vanilla GAN, Deep Convolutional GAN (DCGAN), Bidirectional GAN (BiGAN), and Wasserstein GAN (WGAN) are used. Then, the results of the generated images are evaluated using native-Arabic human and Frechet Inception Distance (FID). The qualitative and quantitative results are provided for the images generation and evaluation. In experimental evaluation, WGAN achieves better results in FID with a value of 96.007. On the other hand, DCGAN achieves better results in native-Arabic human evaluation with a value of 35%.