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Reseach Article

Measuring Performance of Generative Adversarial Networks on Devanagari Script

by Amogh G. Warkhandkar, Baasit Sharief, Omkar B. Bhambure
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 33
Year of Publication: 2020
Authors: Amogh G. Warkhandkar, Baasit Sharief, Omkar B. Bhambure
10.5120/ijca2020920393

Amogh G. Warkhandkar, Baasit Sharief, Omkar B. Bhambure . Measuring Performance of Generative Adversarial Networks on Devanagari Script. International Journal of Computer Applications. 176, 33 ( Jun 2020), 5-9. DOI=10.5120/ijca2020920393

@article{ 10.5120/ijca2020920393,
author = { Amogh G. Warkhandkar, Baasit Sharief, Omkar B. Bhambure },
title = { Measuring Performance of Generative Adversarial Networks on Devanagari Script },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 33 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number33/31414-2020920393/ },
doi = { 10.5120/ijca2020920393 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:06.997125+05:30
%A Amogh G. Warkhandkar
%A Baasit Sharief
%A Omkar B. Bhambure
%T Measuring Performance of Generative Adversarial Networks on Devanagari Script
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 33
%P 5-9
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The working of neural networks following the adversarial philosophy to create a generative model is a fascinating field. Multiple papers have already explored the architectural aspect and proposed systems with potentially good results however, very few papers are available which implement it on a real-world example. Traditionally, people use the famous MNIST dataset as a Hello, World! example for implementing Generative Adversarial Networks (GAN). Instead of going the standard route of using handwritten digits, this paper uses the Devanagari script which has a more complex structure. As there is no conventional way of judging how well the generative models perform, three additional classifiers were built to judge the output of the GAN model. The following paper is an explanation of what this implementation has achieved.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Generator Discriminator Sequential Models Denoising Morphology Thresholding