Optical Character Recognition of Sanskrit Manuscripts using Convolution Neural Networks. Este artículo no está disponible.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.