Optical character recognition (OCR) for the Hindi language faces significant challenges due to the script’s complexity, which includes a large character set, compound characters, intricate diacritical marks, and context-sensitive formations. These issues, coupled with variations in handwriting and difficulties in accurate character segmentation, hinder traditional OCR models from achieving high recognition accuracy. Our research aims to address these challenges through a comparative analysis of novel models for Hindi OCR, with the goal of enhancing recognition efficiency. Initially, we will conduct an in-depth review of current Hindi OCR models, identifying their strengths and limitations to inform our approach. Subsequently, we will develop and optimize new models tailored to Hindi’s unique script using advanced machine learning methods, especially deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).