Recognition Character Sanskrit Using Convolution Neural Network

Abstract

This research presents a pioneering approach using Convolutional Neural Networks (CNNs) for character recognition in Sanskrit, a language renowned for its intricate script and diverse character set. Addressing challenges posed by Sanskrit's complex script and historical variations in writing styles, we developed a CNN-based model that undergoes meticulous preprocessing to enhance image quality and normalize writing styles. Trained on a substantial dataset of annotated Sanskrit characters, our model showcases remarkable accuracy in recognizing Sanskrit characters, even amidst noise and diverse writing styles. This achievement holds significant implications for digitizing ancient manuscripts, aiding linguistic research, and preserving cultural heritage. Automating Sanskrit character recognition accelerates the analysis of Sanskrit texts, offering insights into linguistic evolution, cultural practices, and historical narratives. Moreover, this research lays a foundation for advancing character recognition techniques in complex scripts and languages, fostering opportunities for preserving and exploring diverse cultural heritages worldwide.