In this study, we explore the efficacy of ResNet models in detecting handwritten text in the Telugu language, a domain with limited research. Our objective was to assess the adaptability and accuracy of these models in a challenging script recognition context. We employed a comprehensive dataset of Telugu handwritten characters, applying various ResNet architectures to evaluate their performance. The results reveal a significant improvement in detection accuracy, with the ResNet-50 model achieving the highest word accuracy rate of 84% surpassing previous benchmarks in Telugu text recognition. These findings not only demonstrate the robustness of ResNet models in handling complex scripts but also open avenues for further research in indigenous language processing. The study underscores the potential of deep learning models in enhancing text detection accuracy, marking a notable contribution to the field of optical character recognition for under-researched languages.