Survey on Handwritten Digit Recognition using Machine Learning
DOI:
https://doi.org/10.26438/ijcse/v6si5.96100Keywords:
Handwritten Digits, Vector Machine, Neural Networks, ConvolutionAbstract
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
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
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
