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

Abstract

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.