Denoising Dirty Document using Autoencoder

Authors

  • Imran M Computer Science and Engineering, Neil Gogte Institute of Technology (NGIT), Affiliated to Osmania University, Survey No-35, Peerzadiguda Road, Kachawanisingaram, Uppal, Hyderabad, India
  • T Sita Mahalakshmi Department of Computer Science and Engineering, GITAM Institute of Technology, Andhra Pradesh, India
  • MD Venkata Prasad Research Scholar (Regd No: 1260316406), Dept. of Computer Science and Engineering, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India
  • Kumar Kopparty Research Scholar (Regd No: 41900148), Dept. of Computer Science and Engineering, LPU (Lovely Professional University), Jalandhar - Delhi G.T. Road, Phagwara, Punjab, India

DOI:

https://doi.org/10.26438/ijcse/v7i10.2126

Keywords:

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

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.

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Published

2019-10-31
CITATION
DOI: 10.26438/ijcse/v7i10.2126
Published: 2019-10-31

How to Cite

[1]
M. Imran, S. M. T, V. P. MD, and K. K. V, “Denoising Dirty Document using Autoencoder”, Int. J. Comp. Sci. Eng., vol. 7, no. 10, pp. 21–26, Oct. 2019.

Issue

Vol. 7 No. 10 (2019): IJCSE October Edition

Section

Research Article

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