CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

Machine Recognition of Hand Written Characters using Neural Networks

by Yusuf Perwej, Ashish Chaturvedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 2
Year of Publication: 2011
Authors: Yusuf Perwej, Ashish Chaturvedi
10.5120/1819-2380

Yusuf Perwej, Ashish Chaturvedi . Machine Recognition of Hand Written Characters using Neural Networks. International Journal of Computer Applications. 14, 2 ( January 2011), 6-9. DOI=10.5120/1819-2380

@article{ 10.5120/1819-2380,
author = { Yusuf Perwej, Ashish Chaturvedi },
title = { Machine Recognition of Hand Written Characters using Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 14 },
number = { 2 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number2/1819-2380/ },
doi = { 10.5120/1819-2380 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:57.642234+05:30
%A Yusuf Perwej
%A Ashish Chaturvedi
%T Machine Recognition of Hand Written Characters using Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 2
%P 6-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Even today in Twenty First Century Handwritten communication has its own stand and most of the times, in daily life it is globally using as means of communication and recording the information like to be shared with others. Challenges in handwritten characters recognition wholly lie in the variation and distortion of handwritten characters, since different people may use different style of handwriting, and direction to draw the same shape of the characters of their known script. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters.

References
  1. H. Al-Yousefi and S. S. Udpa, "Recognition of handwritten Arabic characters," in Proc. SPIE 32nd Ann. Int. Tech. Symp. Opt. Optoelectric Applied Sci. Eng. (San Diego, CA), Aug. 1988.
  2. K. Badi and M. Shimura, "Machine recognition of Arabic cursive script" Trans. Inst. Electron. Commun. Eng., Vol. E65, no. 2, pp. 107-114, Feb. 1982.
  3. M Altuwaijri , M.A Bayoumi , "Arabic Text Recognition Using Neural Network" ISCAS 94. IEEE International Symposium on Circuits and systems, Volume 6, 30 May-2 June 1994.
  4. C. Bahlmann, B. Haasdonk, H. Burkhardt., “Online Handwriting Recognition with Support Vector Machine – A Kernel Approach”, In proceeding of the 8th Int. Workshop in Handwriting Recognition (IWHFR), pp 49-54, 2002
  5. Homayoon S.M. Beigi, "An Overview of Handwriting Recognition," Proceedings of the 1st Annual Conference on Technological Advancements in Developing Countries, Columbia University, New York, July 24-25, 1993, pp. 30-46.
  6. Nadal, C. Legault, R. Suen and C.Y, “Complementary Algorithms for Recognition of totally Unconstrained Handwritten Numerals,” in Proc. 10th Int. Conf. Pattern Recognition, 1990, vol. 1, pp. 434-449.
  7. S. Impedovo, P. Wang, and H. Bunke, editors, “Automatic Bankcheck Processing,” World Scientific, Singapore, 1997.
  8. CL Liu, K Nakashima, H Sako and H. Fujisawa, “Benchmarking of state-of- theart techniques,” Pattern Recognition, vol. 36, no 10, pp. 2271– 2285, Oct. 2003.
  9. M. Shi, Y. Fujisawa, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition using gradient and curvature of gray scale image,” Pattern Recognition, vol. 35, no. 10, pp. 2051–2059, Oct 2002.
  10. LN. Teow and KF. Loe, “Robust vision-based features and classification schemes for off-line handwritten digit recognition,” Pattern Recognition, vol. 35, no. 11, pp. 2355–2364, Nov. 2002.
  11. K. Cheung, D. Yeung and RT. Chin, “A Bayesian framework for deformable pattern recognition with application to handwritten character recognition,” IEEE Trans PatternAnalMach Intell, vol. 20, no. 12, pp. 382–1388, Dec. 1998.
  12. IJ . Tsang, IR. Tsang and DV Dyck, “Handwritten character recognition based on moment features derived from image partition,” in Int. Conf. image processing 1998, vol. 2, pp 939–942.
  13. H. Soltanzadeh and M. Rahmati, “Recognition of Persian handwritten digits using image profiles of multiple orientations,” Pattern Recognition Lett, vol. 25, no. 14, pp. 1569–1576, Oct.2004.
  14. FN. Said, RA. Yacoub and CY Suen, “Recognition of English and Arabic numerals using a dynamic number of hidden neurons” in Proc. 5th Int Conf. document analysis and recognition, 1999, pp 237–240
  15. J. Sadri, CY. Suen, and TD. Bui, “Application of support vector machines for recognition of handwritten Arabic/Persian digits,” in Proc. 2th Iranian Conf. machine vision and image procesing, 2003, vol. 1,pp 300–307.
  16. ID. Trier and AK. Jain, “Feature Extraction Methods for Character Recognition- A Survey,” Pattern Recognition, vol. 29, no. 4, pp. 641- 662, April. 1996.
  17. H. Takahashi, “A Neural Net OCR using geometrical and zonal pattern features,” in Proc. 1th. Conf. Document Analysis and Recognition, 1991, pp. 821-828.
  18. L.O. Jimenez, A. Morales-Morell and A. Creus, “Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approachs Using Projection Pursuit, Majority Voting, and Neural Networks,” IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 3, May 1999.
  19. Y. Li, “Reforming the theory of invariant moments for Pattern recognition,” Pattern Recognition Letters, vol. 25, no. 7, pp.723-730, July. 1992.
  20. H.A. Glucksman, “Multicategory of Patterns Represented by High-Order Vectors of Multilevel Measurement,” IEEE Transaction Computer, vol. C-20, no. 12, pp. 1593-1598, Dec. 1997.
  21. Sh. Shahreza, M.H. Khotanzad, A. ,“Recognition Letterpress Works Independent of Size and Displacement with Zernike Moments and Neural Networks”, in Proc. 2th Iranian Conf of Electrical Engineering,1994, Trbiat modares Univ, vol. 5, pp. 417-424.
  22. K. Azmi, R. Kabir and E. Badi, “Recognition Printed Letters wit Zonong Features,” Iran Computer Group, vol. 1, pp. 29-37, 2003.
  23. S.H. Nabavikahrizi, R. Ebrahimpour and E. Kabir, “Application of Combining classifiers for Recognition of Farsi handwritten digits,” in Proc. 3th Iranian Conf. Machine vision and image processing, 2004, vol. 1, pp 115–119.
Index Terms

Computer Science
Information Sciences

Keywords

Machine recognition Handwriting recognition neural networks.