Enhancing Hindi Handwriting Recognition Using Neural Network

With an emphasis on datasets containing the Devanagari script-the writing system used for the Hindi language-this research explores the application of sophisticated neural network techniques to improve the recognition of Hindi handwriting. Optical character recognition systems, which are used in automated form processing, historical manuscript digitisation, and educational programs, depend heavily on handwriting recognition. Conventional recognition techniques frequently lack the robustness and accuracy needed to handle intricate scripts like Devanagari. To increase the accuracy of Hindi handwritten text identification, we employ a number of cutting-edge neural network models in this study, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which are specially designed for the Devanagari script. Our tests show notable gains over baseline models, highlighting the significance of well-optimized network designs and training protocols. We also explore the implications of these results for practical applications and future directions in the field of Hindi handwriting recognition systems.