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JCR Impact Factor: 0.459
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Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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A Blind High-Capacity Wavelet-Based Steganography Technique for Hiding Images into other Images, HAMAD, S., KHALIFA, A., ELHADAD, A.
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Single-phase Multilevel Current Source Inverter with Reduced Device Count and Current Balancing Capability, MOALLEMI KHIAVI, A., FARHADI KANGARLU, M., DAIE KOOZEHKANANI, Z., SOBHI, J., HOSSEINI, S. H.
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Location of Fraudulent Branch Lines or Faults in Short-Length Low Voltage Lines, ESCOBEDO, J., MEDINA, A., HERNANDEZ, J.-C., ALMONACID, G., VIDAL, P.
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  1/2013 - 11

An Effect of Noise in Printed Character Recognition System Using Neural Network

GHEORGHITA, S. See more information about GHEORGHITA, S. on SCOPUS See more information about GHEORGHITA, S. on IEEExplore See more information about GHEORGHITA, S. on Web of Science, MUNTEANU, R. See more information about  MUNTEANU, R. on SCOPUS See more information about  MUNTEANU, R. on SCOPUS See more information about MUNTEANU, R. on Web of Science, GRAUR, A. See more information about GRAUR, A. on SCOPUS See more information about GRAUR, A. on SCOPUS See more information about GRAUR, A. on Web of Science
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Author keywords
backpropagation, character recognition, neural networks, noise perturbation, training algorithm

References keywords
neural(16), networks(9), recognition(8), network(5), character(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-02-28
Volume 13, Issue 1, Year 2013, On page(s): 65 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.01011
Web of Science Accession Number: 000315768300011
SCOPUS ID: 84875336154

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In this article we present the implementation of a neural network model trained with a high noise level using a backpropagation algorithm and the experimental results for printed character recognition, based on the idea of using the primary information by reorganising it in a different format. The values obtained at the outputs of each network are processed by using analysis algorithms designed for this purpose. The suggested model is made up of two neural networks and two analysis modules. In M1 Module we designed a value analysis algorithm for all the outputs of the two neural networks in order to select the best values provided by the networks. The M2 Module also contains a designed algorithm, which assesses the data based on the fact that the highest values are directly correlated with the probability of correctly identifying the characters entered into the networks. Results are obtained for noise of up to 50% applied to the input data. The values obtained at the outputs of the two modules emphasises the increase of the printed character recognition level up to 89.1% for the M1 module and up to 89.8% for the M2 module, the number of errors decreasing vis-a-vis the RNA2 network response from 12.5% to 10.9%, and 10.2%, respectively. In order to set up the hidden layer of 90 neurons, a value of 92% was obtained at the output of the M2 analysis module.The performed model increased the printed character recognition rate by using the same primary information in a different manner. The validity and functionality of the suggested model are confirmed by experimental results.

References | Cited By  «-- Click to see who has cited this paper

[1] G. L. Martin, J. A. Pittman, "Recognizing hand-printed letters and digits using backpropagation learning", Neural Computation, vol. 3, no. 2, pp. 258-267, Summer 1991.

[2] M. Fukumi, S. Omatu, F. Takeda, T. Kosaka, "Rotation-invariant neural pattern recognition system with application to coin recognition", IEEE Trans. Neural Networks, vol. 3, no. 2, 1992.
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 84]

[3] Z. Saidane, C. Garcia, "Automatic scene text recognition using a convolutional neural network", In Workshop on Camera-Based Document Analysis and Recognition, 2007.

[4] Yaoqun Xu, "Effect of white noise on chaotic neural network", Control and Decision Conference, CCDC'09, pp.3229-3234, 2009.

[5] R. M. Zur, Yulei Jiang, L. L. Pesce, K. Drukker, "Noise injection for training artificial neural networks: A comparation with weight decay and early stopping", Medical Physics, vol.36(10), pp.4810-4818, 2009.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]

[6] F. Mamedov, Jamal Fathi Abu Hasna, "Character Recognition using Neural Networks", The 2006 World Congress in Computer Science, Computer Engineering and Applied Computing, ICAI06, 2006.

[7] G. Montavon, G. B. Orr, K. R. Muller, Neural Networks Tricks of the Trade, Springer-Verlag, LNCS7700, ISBN:978-3-642-35288-1, 2012.

[8] Yingqiao Shi, Wenbing Fan, Guodong Shi, "The research of printed character recognition based on neural network", Fourth International Symposium on Parallel Architecture, Algoritms and Programming, pp.119-122, 2011.
[CrossRef] [SCOPUS Times Cited 1]

[9] Li Fuliang, Gao Shuangxi, "Character recognition system board on backpropagation neural network", International Conference on Machine Vision and Human-machine Interface, pp.393-396, 2010.

[10] S. Geman, E. Bienenstock, R. Doursat, "Neural networks and the bias/variance dilemma", Neural Computation 4, pp.1-58, 1992.
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[11] M. I. Jordan, C. M. Bishop, "Neural Networks", ACM Computing Surveys, ISSN:0360-0300, 1996.
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[12] A. Coates, H. Lee, A. Y. Ng, "An analysis of single layer networks in unsupervised feature learning", In AIS-TATS, 2011.

[13] A. I. Galushkin, Neural networks theory, ISBN: 978-3-540-48124-9, Springer-Verlag Berlin Heidelberg, 2007.

[14] S. Gheorghita, R. Munteanu, M. Enache, "Study of Neural Networks to Improve Performance for Character Recognition", Automation Quality and Testing Robotics (AQTR), IEEE International Conference, p. 323-326, 2012.
[CrossRef] [SCOPUS Times Cited 3]

[15] M. Hogan, H. Demuth, M. Beale, Neural network toolbox 6 user’s guide, 2008.

References Weight

Web of Science® Citations for all references: 1,276 TCR
SCOPUS® Citations for all references: 105 TCR

Web of Science® Average Citations per reference: 80 ACR
SCOPUS® Average Citations per reference: 7 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references background updated on 2017-02-25 07:55 in 53 seconds.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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