CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Improving Various Offline Techniques used for Handwritten Character Recognition : A Review

by Rajiv Kumar Nath, Mayuri Rastogi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 18
Year of Publication: 2012
Authors: Rajiv Kumar Nath, Mayuri Rastogi
10.5120/7726-1136

Rajiv Kumar Nath, Mayuri Rastogi . Improving Various Offline Techniques used for Handwritten Character Recognition : A Review. International Journal of Computer Applications. 49, 18 ( July 2012), 11-17. DOI=10.5120/7726-1136

@article{ 10.5120/7726-1136,
author = { Rajiv Kumar Nath, Mayuri Rastogi },
title = { Improving Various Offline Techniques used for Handwritten Character Recognition : A Review },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 18 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number18/7726-1136/ },
doi = { 10.5120/7726-1136 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:33.423072+05:30
%A Rajiv Kumar Nath
%A Mayuri Rastogi
%T Improving Various Offline Techniques used for Handwritten Character Recognition : A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 18
%P 11-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten character recognition is always an advanced area of research in the field of image processing and pattern recognition and there is a large demand for OCR on offline hand written documents. Even though, sufficient studies have performed from history to this era, paper describes the techniques for converting textual content from a paper document into machine readable form. The computer actually recognizes the characters in the document through a revolutionizing technique called Optical Character Recognition (OCR). There are many paper deals with issues such as hand-printed character and cursive handwritten word recognition which describes recent achievements, difficulties, successes and challenges in all aspects of handwriting recognition. Their many papers present a new approach which improves current handwriting recognition systems. Some experimental results are included. Selection of a relevant feature extraction method is probably the single most important factor in achieving high recognition performance with much better accuracy in character recognition systemsn this paper, we describe the formatting guidelines for IJCA Journal Submission.

References
  1. C. Y. Suen, R. Legault, C. Nadal, M. Cheriet and L. Lam, Building a new generation of handwriting recognition systems, Pattern Recognition Letter. 14, 303-315 (April 1993).
  2. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision (PWS Publishing, Books/Cole Pub. Company, 2nd Ed, 1999).
  3. S. Mo, V. J. Mathews, Adaptive, Quadratic Preprocessing of Document Images for Binarization, IEEE Trans. Image Processing 7(7), 992-999, 1998.
  4. J. M. Reinhardt, W. E. Higgins, Comparison between The Morphological Skeleton and Morphological Shape Decomposition, IEEE Trans. Pattern Analysis and Machine Intelligence, 18(9), 951-957, 1996.
  5. I T. Philips, How to Extend and Bootstrap and Existing Data Set with Real-life Degraded Images, in Proc. 5th Int. Conf. Document Analysis and Recognition, 689-693, 1999.
  6. P. K. Sahoo, S. Soltani, A. K. C Wong and Y C Chen, A survey of Thresholding Techniques, Computer Vision, Graphics and Image processing, 41, 233-260, 1998.
  7. Otsu. N ,A threshold selection method from gray level histograms, IEEE Trans. Systems, Man and Cybernetics, 9, 62-66, 1979.
  8. Y. Solihin, C. G. Leedham, Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images, IEEE Trans. Pattern Recognition and Machine Intelligence, 21(8), 761-768, 1999.
  9. J. Saula, M. Pietikainen, Adaptive Document Image Binarization, Pattern Recognition, 33(2), 225-236, 2000.
  10. B D. Trier, A. K. Jain, Goal Directed Evaluation of Binarization Methods, IEEE Trans. Pattern Recognition and Machine Intelligence, 17(12), 1191-1201, 1995.
  11. Y. Yang, H. Yan, An Adaptive Logical Method for Binarization of Degraded Document Images, Pattern Recognition, 33(5), 787-807, 2000.
  12. L. Lam, S. W. Lee and C. Y. Suen, Thinning Methodologies: A Comprehensive Survey, IEEE Transaction Pattern Analysis and Machine Intelligence, 14(9), 869-885, 1992.
  13. M. Chen, X. Ding, A Robust Skew Detection Algorithm For Grayscale Document Image, in Proc. 5th Int. Conf. Document Analysis and Recognition, ,617-620, 1999.
  14. E. Oztop, A. Mulayim, V. Atalay, F. T. Yarman-Vural, Repulsive Attractive Network for Baseline Extraction on Document Images, Signal Processing, 75(1),1-10, 1999.
  15. S. Madhvanath, G. Kim, V. Govindaraju, Chain code Contour Processing for Handwritten Word Recognition, IEEE Trans. Pattern Recognition and Machine Intelligence, 21(9), 928-932, 1999.
  16. M. Cote, E. Lecolinet, M. Cheriet, C. Y. Suen, Reading of Cursive Scripts Using A Reading Model and Perceptual Concepts, The PERCEPTO System, Int. Journal Document Analysis and Recognition, 1(1), 3-17, 1998.
  17. S. W. Lee, Y. J. Kim, Direct Extraction of Topographic Features for Gray Scale Character Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, 17(7), 724-729, 1995.
  18. J Pradeep, E. Srinivasan And S. Himavathi, Diagonal Based Feature Extraction For Handwritten Alphabets Recognition System Using Neural Network, International Journal of Computer Application, 8(9), 2010.
  19. 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), 331-339, 2012.
  20. K. S. Prasanna Kumar ,Optical Character Recognition (OCR) for Kannada numerals using Left Bottom 1/4th segment minimum features extraction ,IJCTA,3(1),221-225,2012.
  21. Anita Pal & Dayashankar Singh, Handwritten English Character Recognition Using Neural Network, International Journal of Computer Science & Communication 1(2), 141-144, 2010.
  22. Velappa Ganapathy, and Kok Leong Liew ,Handwritten Character Recognition Using Multiscale Neural Network Training Technique, World Academy of Science, Engineering and Technology 39 2008.
  23. Dr. Pankaj Agarwal, Hand-Written Character Recognition Using Kohonen Network, IJCST, 2(3), 112-115, 2011.
  24. B. V. Dhandra, Mallikarjun Hangarge, Gururaj Mukarambi, Spatial Features for Handwritten Kannada and English Character Recognition, IJCA Special Issue on Recent Trends in Image Processing and Pattern Recognition, 3(3), 146-151, 2010.
  25. Mohamed Cheriet, Nawwaf Kharma, Cheng-Lin Liu, Ching Y. Suen, Character Recognition Systems: A Guide for students and Practitioners, (John Wiley & Sons, Inc. , Hoboken, New Jersey, 2007).
  26. 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.
  27. Dr. Yadana Thein , San Su Su Yee, High Accuracy Myanmar Handwritten Character Recognition using Hybrid approach through MICR and Neural Network ,IJCSI International Journal of Computer Science Issues, 7(6), November 2010.
  28. P. D. Gader, B. Forester, M. Hansberger, A. Gillies, B. Mitchell, M. Whalen, and T. Yocum, Recognition of Handwritten Digits Using Template and Model Matching, Pattern Recognition, 24(5), 421-431, 1991.
  29. AK. Jain, D. Zongker, Representation and Recognition of Handwritten Digits Using Deformable Templates, IEEE Trans. Pattern Analysis and Machine Intelligence, 19(12), 1386-1391, 1997.
  30. C. C. Tappert, Cursive Script Recognition by Elastic Matching, IBM Journal of Research and Development, 26(6), 765-771, 1982.
  31. P. A. Devijer, J. Kittler, Pattern Recognition: A Statistical Approach, Prentice Hall, 1982.
  32. S. Smith, M. Borgoin, K. Sims, H. Voorhees, Handwritten Character Classification Using Nearest Neighbor in Large Databases, IEEE Trans. Pattern Recognition and Machine Intelligence, 16(9), 915-919,1994.
  33. AK. Jain, J. Mao, and K. M. Mohiuddin, Artificial Neural Networks: A Tutorial, IEEE Computer, 31-44, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Image Acquisition Off-Line & Online Handwriting Character Recognition Segmentation and Training

Powered by PhDFocusTM