The biggest challenge in the field of image processing is to recognize documents both in printed and handwritten format. Optical Character Recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. Development of OCRs for Indian script is an active area of research today. We are making an attempt to develop the OCR system for Oriya language, which is the official language of Orissa. Oriya language present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. In this paper, we argue that a number of automatic and semi-automatic tools can ease the development of recognizers for new font styles and new scripts. We discuss briefly and show how they have helped build new OCRs for the purpose of recognizing Oriya script. We have used the Back propagation Neural Network for efficient recognition where the errors were corrected through back propagation and rectified neuron values were transmitted by feed-forward method in the neural network of multiple layers, i.e. the input layer, the output layer and the middle layer or hidden layers.
Mishra, Soumya; Nanda, Debashish; and Mohanty, Sanghamitra
"Oriya Character Recognition using Neural Networks,"
International Journal of Computer and Communication Technology: Vol. 3:
1, Article 7.
Available at: https://www.interscience.in/ijcct/vol3/iss1/7