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Reseach Article

Assamese Digit Recognition with Feed Forward Neural Network

by Kalyanbrat Medhi, Sanjib Kr. Kalita
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 1
Year of Publication: 2015
Authors: Kalyanbrat Medhi, Sanjib Kr. Kalita
10.5120/19154-0587

Kalyanbrat Medhi, Sanjib Kr. Kalita . Assamese Digit Recognition with Feed Forward Neural Network. International Journal of Computer Applications. 109, 1 ( January 2015), 34-40. DOI=10.5120/19154-0587

@article{ 10.5120/19154-0587,
author = { Kalyanbrat Medhi, Sanjib Kr. Kalita },
title = { Assamese Digit Recognition with Feed Forward Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 1 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number1/19154-0587/ },
doi = { 10.5120/19154-0587 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:43:41.331857+05:30
%A Kalyanbrat Medhi
%A Sanjib Kr. Kalita
%T Assamese Digit Recognition with Feed Forward Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 1
%P 34-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this paper is to design a recognizer to recognize Assamese digits using feed forward neural network. The recognizer crops the individual digits of the image using bounding box method and extracts the feature. In the present study zoning is used to obtain necessary feature vector. This feature is provided as input to the classifier and the network is trained with backpropagation training algorithm with two hidden layer. The recognition rate of printed digits is 98%, including multi size, bold and italics fonts. In case of handwritten digits recognition rate is 70. 6%.

References
  1. Available:http://tdil-dc. in/index. php?option=com_vertical&parentid=77 Accessed on 1st June 2014.
  2. K. Medhi, S. K. Kalita, "Recognition of assamese handwritten numerals using mathematical morphology", Advance Computing Conference (IACC), 2014 IEEE International, India, February 2014, pp. 1076-1080
  3. S. Naz, K. Hayat, M. I. Razzak, et al. : "The optical character recognition of Urdu-like cursive scripts", Pattern Recognition, 2014, vol. 47, pp. 1229-1248
  4. B. Yegnarayana, Artificial Neural Networks, Prentice Hall of India, 2004.
  5. K. L. Du, M. N. S. Swamy, "Neural Networks in a Soft computing Framework", Springer-Verlag London Limited, 2006
  6. D. Impedovo and G. Pirlo , "Zoning methods for handwritten character recognition: A survey", Pattern Recognition vol. 47, pp. 969–981, 2014
  7. J. Park, V. Govindaraju, S. N. Srihari, OCR in a hierarchical feature space, IEEE Transactions on PAMI22, vol. 4, pp. 400–407, 2000
  8. D. Sharma, D. Gupta, "Isolated handwritten digit recognition using adaptive unsupervised incremental learning technique", International Journal of Computer Applications vol. 7, pp. 27–33, 2010
  9. R. Sharma, A. Jain, R. Sharma and J. Wadhwa, "Character And Digit Recognition Aided by Mathematical Morphology", International Journal of Computer Technology & Applications, vol. 4, pp. 828-832, 2013
  10. V. V. Kumar, A. Srikrishna , B. R. Babu and M. R. Mani, "Classification and recognition of handwritten digits by using mathematical morphology", Sadhana vol. 35, pp. 419–426, 2010
  11. U. Bhattacharya, B. B. Chaudhuri, "A majority voting scheme for multi resolution recognition of hand printed numerals", in: Proceedings of the 7th International, Conference on Document Analysis and Recognition, Edinburgh, Scotland, pp. 16–20, 2003
  12. A. Choudhury and J. Mukherjee, "An Approach towards Recognition of Size and Shape Independent Bangla Handwritten Numerals", International Journal of Science and Research (IJSR), pp. 223-226, 2013
  13. R. A. Peters, T. N. Nashville, A new algorithm for image noise reduction using mathematical morphology, IEEE Transactions on Image Processing, 4 , (1995) 554-568
  14. H. K. Kwag, S. H. Kim, S. H. Jeong, G. S. Lee, Efficient skew estimation and correction algorithm for document images, Image and Vision Computing, 20 (2002) 25–35
  15. Y. Caoa, S. Wangb, H. Lia, Skew detection and correction in document images based on straight-line fitting, Pattern Recognition Letters, 24 (2003) 1871–1879
  16. H. Liua, Q. Wua, H. Zhaa, X. Liuc, Skew detection for complex document images using robust borderlines in both text and non-text regions", Pattern Recognition Letters, 29 (2008) 1893-1900
  17. U. Mathur, R. Sharma, N. Srivastava, Script independent angular skew detection and correction algorithms, Signal Processing and Communication (ICSC), 2013 International Conference on, (2013) 466-469
  18. I. Marosi, Industrial OCR approaches: Architecture, algorithms, and adaptation techniques, In Proc. of SPIE, 6500 (2007) 1-10
  19. D. Svozil, V. Kvasnicka, J. Pospichal, Introduction to multi-layer feed-forward neural networks, Chemometrics and Intelligent Laboratory Systems, 39 (1997) 43-62
  20. C. M. Bishop, Neural Networks for Pattern Recognition, Claderon Press, Oxford, 1995.
  21. http://in. mathworks. com/help/images/noise-removal. html?nocookie=true
  22. N. Sankaran and C. V Jawahar , " Recognition of printed Devanagari text using BLSTM Neural Network", Pattern Recognition (ICPR), 21st International Conference on, Tsukuba, pp. 322 – 325, 2012
  23. K. S. Siddharth, M. Jangid, R. Dhir, R. Rani, "Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features", International Journal on Computer Science and Engineering, Vol. 3, pp. 2332-2345, 2011
  24. H. R Mamatha, S. Sucharitha and K. Srikanta Murthy, "Multi-font and Multi-size Kannada Character Recognition based on the Curvelets and Standard Deviation", International Journal of Computer Applications, Vol. 35, Dec. 2011
  25. A. Sampath, C. Tripti, V. Govindaru, "Freeman code based online handwritten character recognition for Malayalam using Back propagation neural networks", Advance computing: An international journal, Vol. 3, pp. 51-58, Jul. 2012
  26. A. Kokku and S. Chakravarthy, "A Complete OCR System for Tamil Magazine Documents", Guide to OCR for Indic Scripts, Springer London, pp. 147-162, 2010
  27. S. Dewan, S. Chakravarthy, "A System for Offline Character Recognition Using Auto-encoder Networks", 19th International Conference, ICONIP 2012, Doha, Qatar, Nov. 2012, Vol. 7666, pp. 91-99, 2012
  28. N. Otsu, A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1), pp. 62-66,1979
  29. S. Bag, G. Harit, P. Bhowmick, "Recognition of Bangla compound characters using structural decomposition", Pattern Recognition, Vol. 47, pp. 1187–1201, 2014
  30. S. Barman, D. Bhattacharyya, S. Jeon, T. Kim, H. Kim, "A New Experiment on Bengali Character Recognition", International Conference, UCMA 2010, Miyazaki, Japan, Jun. 2010. Proceedings, pp. 20-28
  31. A. Choudhury and J. Mukherjee, "An Approach towards Recognition of Size and Shape Independent Bangla Handwritten Numerals", International Journal of Science and Research (IJSR), Vol. 2, pp. 223-226, Jan. 2013
  32. N. Das, K. Acharya, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri, "A Novel GA-SVM Based Multistage Approach for Recognition of Handwritten Bangla Compound Characters", Advances in Intelligent and Soft Computing, Vol. 132, pp. 145-152, 2012
  33. M. Hangarge, G. Mukarambi and B. V. Dhandra, "South Indian Handwritten Script Identification at Block Level from Trilingual Script Document Based on Gabor Features", Proceedings of the First International Conference, ICMCCA, Dec. 13-15, 2012, Vol. 213, pp. 25-33
  34. S. Dewan, S. Chakravarthy, "A System for Offline Character Recognition Using Auto-encoder Networks", 19th International Conference, ICONIP 2012, Doha, Qatar, Nov. 2012, Vol. 7666, pp. 91-99, 2012
  35. V. Rasagna, K. J. Jinesh and C. V. Jawahar, "On Multifont Character Classification in Telugu", International Conference, ICISIL 2011, Patiala, India, Mar. , 2011, Vol. 139, pp. 86-91
Index Terms

Computer Science
Information Sciences

Keywords

Assamese digits Recognition Feed Forward Neural Network Zoning Back Propagation