Exploring cGANsfor Urdu Alphabets and Numerical System Generation

Urdu ligatures play a crucial role in text representation and processing, especially in Urdu language applications. While extensive research has been conducted on handwritten characters in various languages, there is still a significant gap in studying raster-based generated images of Urdu characters. This paper presents a generative model designed to produce high-quality samples that closely resemble yet differ from existing datasets. Utilizing the power of Generative Adversarial Networks (GANs), the model is trained on a diverse dataset comprising 40 classes of Urdu alphabets and 20 classes of numerals (both modern and Arabic-style), with each class containing 1,000 augmented images to capture variations. The generator network creates synthetic Urdu character samples based on class conditions, while the discriminator network evaluates their similarity to real datasets. The model’s effectiveness is assessed using key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID). The results confirm that the proposed GAN-based approach achieves high fidelity and structural accuracy, making it highly valuable for applications in text digitization and Optical Character Recognition (OCR).