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Neural Network based Approach for Recognition of Text Images

by Gaurav Kumar, Pradeep Kumar Bhatia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 14
Year of Publication: 2013
Authors: Gaurav Kumar, Pradeep Kumar Bhatia
10.5120/10146-4963

Gaurav Kumar, Pradeep Kumar Bhatia . Neural Network based Approach for Recognition of Text Images. International Journal of Computer Applications. 62, 14 ( January 2013), 8-13. DOI=10.5120/10146-4963

@article{ 10.5120/10146-4963,
author = { Gaurav Kumar, Pradeep Kumar Bhatia },
title = { Neural Network based Approach for Recognition of Text Images },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 14 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number14/10146-4963/ },
doi = { 10.5120/10146-4963 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:46.301985+05:30
%A Gaurav Kumar
%A Pradeep Kumar Bhatia
%T Neural Network based Approach for Recognition of Text Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 14
%P 8-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size of the characters. Multiple types of handwriting styles from different persons are considered in this work. An image with higher resolution will certainly take much longer time to compute than a lower resolution image. In the practical image acquisition systems and conditions, shape distortion is common processes because different people's handwriting has different shape of characters. The process of recognizing character recognition in this work has been divided into 2 phases. In the first phase, Image preprocessing is done in which image is firstly converted into binary form based on some threshold value obtained through Otsu's method. After that removal of noise is done using median filter. After that feature extraction takes place that is done here through Fourier descriptor method using Fourier transform and correlation between template made through training data and test data is obtained. A multilayer feed forward neural network is created and trained through Back Propagation algorithm. After the training, testing is done to match the pattern with test data. Results for various convergence objective of neural network are obtained and analyzed.

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Index Terms

Computer Science
Information Sciences

Keywords

Character Recognition Image Processing MatLab Neural Network

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