Improved Recognition of Telugu Handwritten Characters Through Synthetic Data Generation and Attention-Based Modelling

Abstract

This study presents an advanced framework for Telugu handwritten character recognition by integrating Conditional Generative Adversarial Networks (cGANs) with Vision Transformer (ViT) architectures. Critical issues with Telugu scripts, such as intricate character structures, significant inter-writer variability, and a lack of annotated handwritten data, are addressed by the suggested method. While the Vision Transformer utilizes self-attention mechanisms to capture long-range spatial dependencies and global contextual features necessary for accurate recognition, cGAN-based synthetic data augmentation is employed to enhance dataset diversity and mitigate class imbalance. The proposed system outperforms several current CNN-, RNN-, and heuristic-based techniques, achieving character recognition accuracy of 97.89% and word recognition accuracy of 97.34%, as determined through extensive experiments conducted on real and synthetic handwritten datasets. Stable performance under noisy and real-world conditions is further confirmed by robustness analysis. The outcomes confirm the efficacy of integrating transformer-based learning with generative AI, creating a dependable and scalable OCR solution for low-resource Indic scripts, such as Telugu.