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Reseach Article

Offline Handwritten Character Recognition using Neural Network

Published on September 2016 by Pankaj Rani, Sushil Kakkar, Shweta Rani
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 1
September 2016
Authors: Pankaj Rani, Sushil Kakkar, Shweta Rani
767709bd-6d64-48d3-9d71-edc202800508

Pankaj Rani, Sushil Kakkar, Shweta Rani . Offline Handwritten Character Recognition using Neural Network. International Conference on Advances in Emerging Technology. ICAET2016, 1 (September 2016), 13-16.

@article{
author = { Pankaj Rani, Sushil Kakkar, Shweta Rani },
title = { Offline Handwritten Character Recognition using Neural Network },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 13-16 },
numpages = 4,
url = { /proceedings/icaet2016/number1/25878-t022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Pankaj Rani
%A Sushil Kakkar
%A Shweta Rani
%T Offline Handwritten Character Recognition using Neural Network
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 1
%P 13-16
%D 2016
%I International Journal of Computer Applications
Abstract

The offline character recognition is very useful software in the field of research. Authoritative field of research has made by character recognition due to its need in various fields of research as in banks, post offices to fulfill all recognition requirements. This paper is an exploration on the different scripts including Mathematical digits, Hindi consonants and vowels, Gurumukhi characters as well as numerals. Different handwritten samples of all these language scripts are taken from different persons in a good environment. First of all pre-processing including different operations for the reduction of noise is operated. The pre-processed documents are then segmented for the feature extraction. In this technique, different features like lines, corners etc. are to be extracted. Number of hidden layers taken are 15. Then neural network is trained for the testing of all these handwritten samples. Out of 100% samples, 90% are used for training, 5% are for testing and the remaining of the samples are used for validation. Comparison of recognition rates achieved on different scripts is accessible. Out of all samples taken, highest accuracy is achieved on the Gurumukhi script which is 99. 9%.

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Index Terms

Computer Science
Information Sciences

Keywords

Handwritten Recognition Pattern Recognition Neural Network And Feature Extraction