Improvement in Efficiency of Recognition of Handwritten Telugu Script
K. Vijay Kumar1, R.Rajeshwara Rao2
1K. Vijay Kumar , Asst. prof (cse), Vivekananda Institute of Technology and Science SET, Karimnagar, AP, India.
2R.Rajeshwara Rao, Assoc. prof (cse), JNTUK University College Of Engineering ,Vizianagaram, AP, India.
Manuscript received on December 11, 2013. | Revised Manuscript Received on December 21, 2013. | Manuscript published on December 20, 2013. | PP: 1-4 | Volume-2, Issue-1, December 2013. | Retrieval Number: A0373122113/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we discuss Multi-Layer Perceptron (MLP)networks for recognition of handwritten Telugu Characters (HTCR). For training of MLP networks error back propagation algorithm is used. We present an automatic HTCR system using MLP networks. Many techniques have been used to recognize Telugu characters but accuracy of recognition is not so much efficient as efficiency of recognition of other scripts. Multilayer Perceptron neural network is used for recognition of characters of other scripts. We would like to use MLP for HTCR so that recognition can be done accurately and efficiently.
Keywords: Handwritten Telugu character recognition (HTCR), Optical character recognition (OCR), Handwritten character recognition (HCR) multilayer Perceptron (MLP) neural network.