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Denoising Dirty Document using Autoencoder

Mohammad Imran1 , T. Sita Mahalakshmi2 , M.D. Venkata Prasad3 , V. Kumar Kopparty4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-10 , Page no. 21-26, Oct-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i10.2126

Online published on Oct 31, 2019

Copyright © Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty, “Denoising Dirty Document using Autoencoder,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.21-26, 2019.

MLA Style Citation: Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty "Denoising Dirty Document using Autoencoder." International Journal of Computer Sciences and Engineering 7.10 (2019): 21-26.

APA Style Citation: Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty, (2019). Denoising Dirty Document using Autoencoder. International Journal of Computer Sciences and Engineering, 7(10), 21-26.

BibTex Style Citation:
@article{Imran_2019,
author = {Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty},
title = {Denoising Dirty Document using Autoencoder},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2019},
volume = {7},
Issue = {10},
month = {10},
year = {2019},
issn = {2347-2693},
pages = {21-26},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4888},
doi = {https://doi.org/10.26438/ijcse/v7i10.2126}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.2126}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4888
TI - Denoising Dirty Document using Autoencoder
T2 - International Journal of Computer Sciences and Engineering
AU - Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty
PY - 2019
DA - 2019/10/31
PB - IJCSE, Indore, INDIA
SP - 21-26
IS - 10
VL - 7
SN - 2347-2693
ER -

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Abstract

An autoencoder is an unsupervised machine learning algorithm [12] that applies back propagation, setting the target values to be equal to the inputs. Deep autoencoders are used to reduce the size of our inputs into a minor representation. If anyone needs the original data, they can reconstruct it from the compressed data.The input seen by the autoencoder is not the raw input but a stochastically corrupted version. A denoising autoencoder is thus trained to reconstruct the original document from the noisy version.In the implementation of Deep autoencoders we have trained the algorithm with noisy and cleaned document images; we generated a model which helps us in removing noise or unnecessary interruption from the documents. Document denoising can be achieved with the deep learning model which automatically learns the discriminative features necessary for classification of input images.

Key-Words / Index Term

document denoising,deep autoencoder,supervised learning, deep learning ,classification,cleaned and noisy images

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