Digit Recognition Using Neural Network

The application of neural networks to digital recognition through a relatively easy-to-understand by the general public cannot be over emphasize. This paper investigated the several techniques used for preprocessing the handwritten digits, as well as several ways in which neural networks are used for the digital recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.

Neural-Network, Artificial-neural, Segmentation, Dgital Recognition, Feed-Forward and Back-Propagation

1. R. Plamondon and S. N. Srihari, On-line and off-line handwritten character recognition: A comprehensive survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, no. 1, 2000, 63-84. [Google Scholar] [Crossref]

2. Xiao-Xiao Niu and Ching Y. Suen, A novel hybrid CNN–SVM classifier for recognizing handwritten digits, ELSEVIER, The Journal of the Pattern Recognition Society, Vol. 45, 2012, 1318– 1325. [Google Scholar] [Crossref]

3. Diego J. Romero, Leticia M. Seijas, Ana M. Ruedin, Directional Continuous Wavelet Transform Applied to Handwritten Numerals Recognition Using Neural Networks, JCS&T, Vol. 7 No. 1, 2007. [Google Scholar] [Crossref]

4. V.N. Manjunath Aradhya, G. Hemantha Kumar and S. Noushath, Robust Unconstrained Handwritten Digit Recognition using Radon Transform, IEEE-ICSCN, 2007, 626-629. [Google Scholar] [Crossref]

5. Zhu Dan and Chen Xu, The Recognition of Handwritten Digits Based on BP Neural Network and the Implementation on Android, Fourth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), 2013, 1498-1501. [Google Scholar] [Crossref]

6. Adriano Mendes Gil, Cícero Ferreira Fernandes Costa Filho, Marly Guimarães Fernandes Costa, Handwritten Digit Recognition Using SVM Binary Classifiers and Unbalanced Decision Trees, Image Analysis and Recognition, Springer, 2014, 246-255. [Google Scholar] [Crossref]

7. Al-Omari F., Al-Jarrah O, Handwritten Indian numerals recognition system using probabilistic neural networks, Adv. Eng. Inform, 2004, 9–16. [Google Scholar] [Crossref]

8. Junchuan Yanga, ,Xiao Yanb and Bo Yaoc, Character Feature Extraction Method Based on Integrated Neural Network, AASRI Conference on Modelling, Identification and Control, ELSEVIER, AASRI Pro. [Google Scholar] [Crossref]

9. Nazri Mohd Nawi, Walid Hasen Atomi, and M. Z. Rehman, The Effect of Data Pre-Processing on Optimized Training of Artificial Neural Networks, Procedia Technology, ELSEVIER, 11, 2013, 32 – 39. [Google Scholar] [Crossref]

10. K. Gaurav and Bhatia P. K., “Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition”, 2nd International Conference on Emerging Trends in Engineering & Management, ICETEM, 2013. [Google Scholar] [Crossref]

11. Salvador España-Boquera, Maria J. C. B., Jorge G. M., and Francisco Z. M., “Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 4, April 2011. [Google Scholar] [Crossref]

12. U. Pal, T. Wakabayashi, and F. Kimura, “Handwritten numeral recognition of six popular scripts,” Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol.2, pp.749-753, 2007. [Google Scholar] [Crossref]

13. Anita Pal & Dayashankar Singh, “Handwritten English Character Recognition Using Neural,” Network International Journal of Computer Science & Communication. Vol. 1, No. 2, July-December 2010, pp. 141-144. [Google Scholar] [Crossref]

14. J. Pradeep, E. Srinivasan and S. Himavathi, “Diagonal Based Feature Extraction For Handwritten Alphabets Recognition System Using Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, Feb 2011. [Google Scholar] [Crossref]

15. Brakensiek, J. Rottland, A. Kosmala and J. Rigoll, “Offline Handwriting Recognition using various Hybrid Modeling Techniques & Character N-Grams”, Available at http://irs.ub.rug.nl/dbi/4357a84695495. [Google Scholar] [Crossref]

16. Reena Bajaj, Lipika Dey, and S. Chaudhury, “Devnagari numeral recognition by combining decision of multiple connectionist classifiers”, Sadhana, Vol.27, part. 1, pp.-59-72, 2002. [Google Scholar] [Crossref]

17. Sandhya Arora, “Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition”, IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA, December 2008. [Google Scholar] [Crossref]

18. Mohammed Z. Khedher, Gheith A. Abandah, and Ahmed M. AlKhawaldeh, “Optimizing Feature Selection for Recognizing Handwritten Arabic Characters”, Proceedings of World Academy of Science Engineering and Technology, vol. 4, February 2005 ISSN 1307-6884. [Google Scholar] [Crossref]

19. Sushree Sangita Patnaik and Anup Kumar Panda, “Particle Swarm Optimization and Bacterial Foraging Optimization Techniques for Optimal Current Harmonic Mitigation by Employing Active Power Filter Applied Computational Intelligence and Soft Computing”, Volume 2012, Article ID 897127. [Google Scholar] [Crossref]

20. Rafael Gonzalez, C., E. Richard Woods and L. Steven Eddins, 2003. Digital Image Processing using MATLAB. 2nd Edn., Prentice Hall, USA., ISBN: 10: 0130085197, pp: 624. [Google Scholar] [Crossref]

21. Rafael C. Gonzalez and Richard E. Woods, 2002. Digital Image Processing. 2nd Edn., Prentice-Hall, Inc., USA., ISBN: 10: 0201180758, pp: 28-29. [Google Scholar] [Crossref]

22. Grother, P.J. and G.T. Candela, 1993. Comparison of handprinted digit classifiers. Technical Report NISTIR 5209, NIST. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.8934 [Google Scholar] [Crossref]

23. Pfister, M., 1996. Learning algorithms for feedforward neural networks design, combination, and analysis. Number 435 in Fortschrittberichte Reihe 10. VDI-Verlag, Diisseldorf. http://en.scientificcommons.org/6488386 [Google Scholar] [Crossref]

24. Lee, S.W. and Y.J. Kim, 1995. Direct extraction of topographic features for grayscale character recognition. IEEE. Trans. Patt. Anal. Mach. Intell., 17: 724-729. DOI: 10.1109/34.391416 [Google Scholar] [Crossref]

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