This study delves into the development of a robust system aimed at recognizing handwritten characters in the Gujarati script, a language of rich cultural heritage and linguistic diversity. Through meticulous data collection and curation efforts, a diverse dataset comprising 47 distinct classes of characters was assembled, spanning from elementary consonants to complex conjuncts. Leveraging the powerful EfficientNetB0 architecture, the model underwent rigorous training and fine-tuning, incorporating advanced data pre-processing techniques and augmentation strategies to enhance its performance and adaptability. The trained model demonstrated promising outcomes, achieving an overall accuracy of 90% on the validation dataset. Notably, detailed analysis via the classification report underscored the model’s ability to discern between different characters, with high precision and recall rates observed across various classes. This research underscores the significance of employing deep learning methodologies in advancing handwritten character recognition systems, thus contributing to the broader landscape of language technology and digital humanities.