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Survey on Handwritten Digit Recognition using Machine Learning

Dr. Narender Kumar1 , Himanshu Beniwal2

Section:Survey Paper, Product Type: Journal Paper
Volume-06 , Issue-05 , Page no. 96-100, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6si5.96100

Online published on Jun 30, 2018

Copyright © Dr. Narender Kumar, Himanshu Beniwal . 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: Dr. Narender Kumar, Himanshu Beniwal, “Survey on Handwritten Digit Recognition using Machine Learning,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.96-100, 2018.

MLA Style Citation: Dr. Narender Kumar, Himanshu Beniwal "Survey on Handwritten Digit Recognition using Machine Learning." International Journal of Computer Sciences and Engineering 06.05 (2018): 96-100.

APA Style Citation: Dr. Narender Kumar, Himanshu Beniwal, (2018). Survey on Handwritten Digit Recognition using Machine Learning. International Journal of Computer Sciences and Engineering, 06(05), 96-100.

BibTex Style Citation:
@article{Kumar_2018,
author = {Dr. Narender Kumar, Himanshu Beniwal},
title = {Survey on Handwritten Digit Recognition using Machine Learning},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {06},
Issue = {05},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {96-100},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=429},
doi = {https://doi.org/10.26438/ijcse/v6i5.96100}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.96100}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=429
TI - Survey on Handwritten Digit Recognition using Machine Learning
T2 - International Journal of Computer Sciences and Engineering
AU - Dr. Narender Kumar, Himanshu Beniwal
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 96-100
IS - 05
VL - 06
SN - 2347-2693
ER -

           

Abstract

Machine learning and deep learning plays an important role in computer technology and artificial intelligence. With the use of deep learning and machine learning, human effort can be reduce in recognizing, learning, predictions and many more areas. This paper presents recognizing the handwritten digits (0 to 9) from the famous MNIST dataset, comparing classifiers like KNN, PSVM, NN and convolution neural network on basis of performance, accuracy, time, sensitivity, positive productivity, and specificity with using different parameters with the classifiers.

Key-Words / Index Term

Handwritten Digits, Vector Machine, Neural Networks, Convolution, Machine Learning

References

[1] Anuj Dutt, Aashi Dutt, `Handwritten Digit Recognition Using Deep Learning`, (IJARCET) Volume 6, Issue 7, July 2017, ISSN: 2278 – 1323, Page number – 990-997
[2] Priya, Rajendra Singh, Dr. Soni Changlani, ‘Handwritten Digit Recognition using Proximal Support Vector Machine’ , Journal of Emerging Technologies and Innovative Research, Volume 4, Issue 04,April 2017, ISSN Number: 2349-5162, Page No: 251-254
[3] Saeed AL-Mansoori, ‘Intelligent Handwritten Digit Recognition using Artificial Neural Network’, Int. Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 5, Issue 5,
( Part -3) May 2015, pp.46-51
[4] Swapna Prava Ekka, ‘Recognition of Handwritten Digits using Proximal Support Vector Machine’, Department of ECE, EThesis NIT, Rourkela, 2014, ID Code - 6066
[5] Gaurav Jain, Jason Ko, ‘Handwritten DigitsRecognition’, Project Report, University of Toronto, 11/21/2008
[6] Fabien Lauera,,ChingY. Suenb, Gérard Blocha, ‘A trainable feature extractor for handwritten digit recognition’, Pattern Recognition Society. Published by Elsevier Ltd., Volume 40, Issue 6, June 2007, Pages 1816-1824, October 2006.
[7] Dan Cires¸anUeli Meier, JurgenSchmidhuber, ‘Multi-column Deep Neural Networks for Image Classification’, Journal reference: CVPR 2012, p. 3642-3649 , Report number: IDSIA-04-12 Feb 2012
[8] Dan ClaudiuCiresan, Ueli Meier, Luca Maria Gambardella, JurgenSchmidhuber, ‘Deep Big Simple Neural Nets Excel on HandwrittenDigit Recognition’, Neural Computation, Volume 22, Number 12, December 2010
[9] Patrice Y. Simard, Dave Steinkraus, John C. Platt, ‘Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis’, Institute of Electrical and Electronics Engineers, Inc.,Inproceedings, ISBN: 0-7695-1960-1, Sept., 2003
[10] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, ‘Gradient-Based Learning Applied to Document Recognition’, Proceedings of the IEEE, v. 86, pp. 2278-2324, 1998.
[11] JurgenSchmidhuber, ‘Deep Learning in Neural Networks: An Overview’, Technical Report IDSIA-03-14, April 2014.
[12] Berend-Jan van der Zwaag, ‘Handwritten Digit Recognition:A Neural Network Demo’, B. Reusch (Ed.): Fuzzy Days 2001, LNCS 2206, pp. 762–771, 2001.
[13] Haider A. Alwzwazy, Hayder M. Albehadili, Younes S. Alwan, Naz E. Islam, ‘Handwritten Digit Recognition Using Convolutional Neural Networks’, IJIRCCE, Vol. 4, Issue 2, February 2016.
[14] Stefan Knerr, LConPersonnaz, and GCrard Dreyfus, ‘Handwritten digit recognition by NeuralNetworks with Single-Layer Training’, IEEE Transactions On Neural Networks, VOL. 3, NO. 6, Nov., 1992
[15] Faisal Tehseen Shah, Kamran Yousaf, ‘Handwritten Digit Recognition Using Image Processing and Neural Networks’, WCE 2007, VOL-1, July 2 - 4, 2007