Deep Learning for Handwritten Hindi Character Recognition: A CNN-Based Implementation

This manuscript delineates a sophisticated framework for the recognition of handwritten Hindi characters, employing diverse deep learning methodologies. Within this study, we proffer a methodology for recognizing handwritten Hindi characters utilizing advanced deep learning techniques, including CNN (Convolutional Neural Network) optimized with RMSprop (Root Mean Square Propagation) and Adam (Adaptive Moment Estimation). The presented system undergoes rigorous training on a substantial collection of database images, subsequently undergoing meticulous testing on samples derived from user-defined datasets. The outcomes of these experiments demonstrate exceptionally elevated recognition results, establishing the efficacy of our proposed technique. Comparative analyses are conducted with other neural network-based algorithm, elucidating the superior performance of our approach.