Integration of Wasserstein GANs and Convolutional Neural Networks for Urdu Handwritten Character Recognition

Aim/Background This study aims to enhance the accuracy and robustness of Urdu Handwriting Character Recognition (UHCR), a task hindered by the limited availability of labeled training data. As Urdu is widely used across South Asia, reliable UHCR systems hold potential for accessibility technologies, linguistic research, and multilingual applications. Methodology A hybrid approach is proposed that combines Convolutional Neural Networks (CNNs) for feature extraction with Wasserstein Generative Adversarial Networks (WGANs) for synthetic data generation. The CNN is employed to capture discriminative features of handwritten Urdu characters, while the WGAN produces high-quality artificial samples to expand the dataset. Additionally, transfer learning from related languages is explored to further improve recognition performance. Results The CNN–WGAN framework achieved higher recognition accuracy compared to conventional CNN models. The synthetic data generated by the WGAN effectively mitigated the limitations of scarce training samples, leading to improved model generalization and robustness. Transfer learning further contributed to performance gains. Discussion The findings demonstrate the effectiveness of integrating generative and discriminative models for low-resource handwriting recognition. The results suggest that WGAN-based augmentation can provide scalable solutions for other low-resource scripts. The potential of transfer learning indicates promising directions for cross-lingual applications in character recognition. Conclusion The proposed CNN–WGAN model significantly improves UHCR by addressing dataset scarcity and enhancing recognition accuracy. This research contributes to advancements in deep learning applications, accessibility technologies, and multilingual character recognition, while encouraging further exploration of generative models in under-resourced languages.