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A Review on Character Recognition Techniques

by Richa Goswami, O. P. Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 7
Year of Publication: 2013
Authors: Richa Goswami, O. P. Sharma
10.5120/14460-2737

Richa Goswami, O. P. Sharma . A Review on Character Recognition Techniques. International Journal of Computer Applications. 83, 7 ( December 2013), 18-23. DOI=10.5120/14460-2737

@article{ 10.5120/14460-2737,
author = { Richa Goswami, O. P. Sharma },
title = { A Review on Character Recognition Techniques },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 7 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number7/14460-2737/ },
doi = { 10.5120/14460-2737 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:58:45.354097+05:30
%A Richa Goswami
%A O. P. Sharma
%T A Review on Character Recognition Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 7
%P 18-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Character Recognition (CR) has been studied from the past several decades, and is still a demanding research topic in the field of pattern recognition and image processing. There is a large demand for Optical Character Recognition on hand written documents. Now days, the rapidly growing computational power enables the implementation of the present Character Recognition methodologies and creates a growing demand on many emerging application fields, which require further advanced methodologies. This paper provides a comprehensive review of existing works in handwritten character recognition based on soft computing technique during the past decade.

References
  1. Nafiz Arica and Fatos T. Yarman-Vural, ?"An Overview of Character Recognition Focused on Off-Line Handwriting", IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, vol. 31, no. 2, pp. 216-233, May 2001.
  2. J. Mantas, "An overview of character recognition methodologies", Pattern Recognition, vol. 19, no. 6, pp. 425 - 430, 1986.
  3. T. S. El-Sheikh and R. M. Guindi, "Computer recognition of arabic cursive script,", Pattern Recognition, vol. 21, no. 4, pp. 293 - 302,1988.
  4. C. Tappert, C. Suen, and T. Wakahara, "The state of the art in online handwriting recognition", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 12, pp. 787 -808, Aug. 1990.
  5. Q. Tian, P. Zhang, T. Alexander, and Y. Kim, "Survey: omnifont-printed character recognition,", in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (K. -H. Tzou & T. Koga, ed. ), vol. 1606 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, pp. 260-268, Nov. 1991.
  6. Y. Ding, F. Kimura, Y. Miyake, and M. Shridhar, "Evaluation and improvement of slant estimation for handwritten words", in Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on, pp. 753 -756, Sept. 1999.
  7. S. M. Lucas, E. Vidal, A. Amiri, S. Hanlon, and J. -C. Amengual, "A comparison of syntactic and statistical techniques for off-line ocr", in Proceedings of the Second International Colloquium on Grammatical Inference and Applications, (London, UK), pp. 168-179, Springer-Verlag, 1994.
  8. K. -F. Chan and D. -Y. Yeung, "Recognizing on-line handwritten alphanumeric characters through exible structural matching", Department of Computer Science, Volume 32, Issue 7 , Pages 1099–1114 ,1999.
  9. S. Mori, C. Suen, and K. Yamamoto, "Historical review of ocr research and development", Proceedings of the IEEE, vol. 80, pp. 1029 - 1058, July 1992.
  10. I. Bazzi, R. Schwartz, and J. Makhoul, "An omnifont open-vocabulary ocr system for english and arabic", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 21, pp. 495 -504, June 1999.
  11. J. Hu, S. G. Lim, and M. K. Brown, "Writer independent on-line handwriting recognition using an hmm approach", Pattern Recognition, vol. 33, no. 1, pp. 133 - 147, 2000.
  12. Alessandro Vinciarelli, ?"Noisy Text Categorization?,", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1882-1889,December 2005.
  13. Ujjwal Bhattacharya, and B. B. Chaudhuri, "Handwritten Numeral Databases of Indian Scripts and MultistageRrecognition of Mixed Numerals", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 03, pp. 444-457, march 2009.
  14. J. Yang and X. B. Li, "Boundary detection using mathematical morphology", Pattern Recognit. Lett. , vol. 16, no. 12, pp. 1287–1296, 1995.
  15. Salvador Espan˜a-Boquera, Maria Jose Castro-Bleda, Jorge Gorbe-Moya, and Francisco Zamora-Martinez "Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 4, pp. 767-779, April 2011.
  16. G. Pirlo and D. Impedovo, "Fuzzy-Zoning-Based Classification for Handwritten Characters", IEEE Transaction on pattern Recognition and Machine Intelligence, vol. 19, no. 04, pp. 780-785, August 2011.
  17. I. S. Oh, J. S. Lee, and C. Y. Suen, "Analysis of class separation and combination of class-dependent features for handwriting recognition", IEEE Trans. Pattern Anal. Machine Intell. , vol. 21, pp. 1089–1094, Oct. 1999.
  18. S. S. Wang, P. C. Chen, andW. G. Lin, "Invariant pattern recognition by moment Fourier descriptor", Pattern Recognition. , vol. 27, pp. 1735–1742, 1994.
  19. Yuefeng Chen, Chunlin Liang, Lingxi Peng and Xiuyu Zhong, "A Handwritten Character Recognition Algorithm based on Artificial Immune?", International Conference on Computer Application and System Modeling (ICCASM ), pp. v12-273-276, 2010.
  20. Yu Yang, "Handwritten Nepali Character Recognition Based on Wavelet Packet Transform and Artificial Immune System", International Conference on Computer Science and Service System, pp. 442-445,August 2011.
  21. Y. C. Chim, A. A. Kassim, and Y. Ibrahim, "Character recognition using statistical moments", Image Vis. Comput. , vol. 17, pp. 299–307, 1999.
  22. Muhammad Sarfraz1 and Ali Taleb Ali Al-Awami, "Arabic Character Recognition using Particle Swarm Optimization with Selected and Weighted Moment Invariants", Sixth International Conference on Signal Processing and its Application,ISSPA, pp. 1-4, May 2007.
  23. C. Suresh Kumar ,Dr. T. Ravichandran , "Handwritten Tamil Character Recognition and Conversion using Neural Network ",International Journal on Computer Science and Engineering 2(7), 2261-2267, 2010.
  24. Y. Tao and Y. Y. Tang, "The feature extraction of Chinese character based on contour information", in Proc. 5th Int. Conf. Document Anal. Recognit. , Bangalore, India, pp. 637–640, 1999.
  25. Ashoka H. N. , Manjaiah D. H. ,Rabindranath Bera ,"Feature Extraction Technique for Neural Network Based Pattern Recognition",International Journal of Computer Science & Engineering (IJCSE) 4(3), pp. 331-339, 2012.
  26. Saurabh Shrivastava and Manu Pratap Singh, "Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets", Applied Soft Computing, vol. 11,pp. 1156–1182, 2011.
  27. Shabana Mehfuz, Gauri Katiyar, "Intelligent Systems for Off-Line Handwritten Character Recognition: A Review" International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume 2, Issue 4, April 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Handwritten Character recognition feature extraction segmentation training and recognition.

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