Optical Character Recognition of Indian Language Manuscripts using Convolutional Neural Networks

Abstract

India's constitution has 22 languages written in 17 different scripts. These materials have a limited lifespan, and as generations pass, these materials deteriorate, and the vital knowledge is lost. This work uses digital texts to convey information to future generations. Optical Character Recognition (OCR) helps extract information from scanned manuscripts (printed text). This paper proposes a simple and effective solution of optical character recognition (OCR) Sanskrit Character from text document images using long short-term memory (LSTM) and neural networks of Sanskrit Characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method using Bidirectional Long Short-Term Memory (BLSTM) architecture for overlapping lines, touching characters in middle and upper zone and half character which would increase the accuracy of the present OCR system for recognition of poorly maintained Sanskrit literature.