Optical Character Recognition of Sanskrit Manuscripts using Convolution Neural Networks

Optical Character Recognition of Sanskrit Manuscripts Using Convolution Neural Networks delves into the cutting-edge application of deep learning for deciphering Sanskrit manuscripts written in Devanagari script. Tackling one of the most challenging tasks in OCR-recognizing Sanskrit's intricate characters and symbols-this work presents a robust system designed to enhance recognition accuracy for scanned text images.By employing advanced architectures such as Convolutional Neural Networks (CNN) Long Short-Term Memory (LSTM) and Bidirectional LSTM alongside traditional classifiers like k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) the research achieves remarkable accuracy rates. Beyond single-touching characters it innovatively addresses overlapping lines connected letters and half-characters providing solutions to limitations in existing systems.With a peak recognition accuracy of 98.64% for mixed Sanskrit text this study is a vital contribution to the preservation and digitization of ancient literature. It opens new doors to computational linguistics ensuring Sanskrit's cultural heritage thrives in the digital age.