The recognition of handwritten characteristics holds great significance in digitalization, particularly in the context of deciphering handwritten letters and numbers. In advanced deep learning, numerous convolution models have been explored to transform handwritten content into a digital format. This study uses the Deep ResNet50 architecture for the recognition of Gujarati characters, utilizing a deeper convolution network with residual connections to enhance feature learning. Identical connections help mitigate vanishing gradient issues inherent in deep ResNet (DResNet) models. The proposed model’s simulation was conducted on two benchmark datasets of handwritten Gujarati characters, achieving an accuracy of 99.71%. Furthermore, these experimental analyses provide a comparative examination of various convolution networks for character recognition. The study also explores different hyperparameters, aiming to enhance feature learning and improve accuracy for handwritten character recognition.