Automatic recognition of off-line handwritten Arabic (Indian) numerals using support vector and extreme learning machines

International Journal of Imaging

Volumn 2, Issue 9 A, 2009, Pages 34-53

  Mahmoud, Sabri A ^a   Olatunji, Sunday O ^a  

^a KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Arabic (Indian) numeral recognition; Handwritten digit recognition; HMM; Normalization; OCR; SVM

Indexed keywords

EID: 77049089511     PISSN: 09740627     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (44)

1. S. Mahmoud, "Recognition of Arabic (Indian) Numerals using Hidden Markov Models", Signal Processing 88, April 2008, pp.844-857.
2. C. Liu, K. Nakashima, H. Sako, H. Fujisawa, "Handwritten digit recognition: Benchmarking of stateof the- art techniques", Pattern Recognition 36, (2003), pp. 2271-2285.
3. M. Shi, Y. Fujisawa, T. Wakabayashi, F. Kimura, "Handwritten numeral recognition using gradient and curvature of gray scale image", Pattern Recognition, 35, (2002), pp. 2051-2059.
4. L.N. Teow, K.F. Loe,"Robust vision-based features and classification schemes for off-line handwritten digit recognition", Pattern Recognition, 35, (2002), pp.2355-2364.
5. K. Cheung, D. Yeung, R.T. Chin, "A Bayesian framework for deformable pattern recognition with application to handwritten character recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (12), (1998), pp.1382-1388.
6. I.J.Tsang, I.R.Tsang, D.V. Dyck, "Handwritten character recognition based on moment features derived from image partition", International Conference on Image Processing, vol. 2, (1998), pp. 939-942.
7. F. Al-Omari,"Hand-written Indian numeral recognition systems using template matching approaches", Proc ACS/IEEE Int. Conf Computer Syst. Appl., (2001);pp. 83-8.
8. F. Al-Omari, O. Al-Jarrah, "Handwritten Indian numerals recognition system using probabilistic neural networks", Advanced Engineering Informatics 18 (2004), pp. 9-16.
9. F. Bousalma,"Structural and fuzzy techniques in the recognition of online Arabic characters", Int. Journal Pattern Recognition and Artificial Intelligence, 13(7),(1999), pp.1027-1040.
10. A. Salah, E. Albaydin, L. Akarun,"A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002;24(3): pp. 420-425.
11. A. Hamid, R. Haraty,"A neuro-heuristic approach for segmenting handwritten Arabic text", Proc, ACS/IEEE Int. Conf Computer Syst. Appl., 2001; pp.110-113.
12. S. Saloum, "Arabic hand-written text recognition", Proc ACS/IEEE Int. Conf Computer Syst. Appl., 2001; pp.106-109.
13. S. Almaadeed, C. Higgens, and D. Elliman, "Recognition of Off-line Handwritten Arabic Words using Hidden Markov Model Approach," ICPR 2002, Quebec City, August 2002, pp. 481-484.
14. S. Almaadeed, C. Higgens, and D. Elliman, "Off-line recognition of handwritten Arabic words using multiple hidden Markov models", Knowledge-Based Systems, Vol. 17, pp. 75-79, 2004.
15. S. Touj, N. Ben Amara, H. Amiri, "Arabic Handwritten Words Recognition Based on a Planar Hidden Markov Model", Int. Arab J. Inf. Technology, vol. 2, no. 4, pp. 318-325, 2005.
16. B. Al-Badr, Sabri A. Mahmoud, "Survey and Bibliography of Arabic Optical Text Recognition," J. of Signal Processing, Vol. 41, No.1, pp.49-77 (Jan. 1995).
17. L. Lorigo, V. Govindaraju, "Offline Arabic Handwriting Recognition: A Survey", EEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 712-724, May 2006.
18. A. Nabawi and S. Mahmoud, "Arabic Optical Text Recognition: A Classified Bibliography" , Engineering Research Bulletin, Minufiyah University, Egypt, Vol. 23, No. 1, Jan. 2000, pp. 79-131.
19. I. Bazzi, C. LaPre, J. Makhoul, and R. Schwartz, "Omnifont and Unlimited Vocabulary OCR for English and Arabic", Proc. Int'l Conf. Document Analysis and Recognition, vol. 2, pp. 842-846, Ulm, Germany, 1997.
20. I. Bazzi, R. Schwartz, and J. Makhoul,"An Omifont Open-Vacabulary OCR system for English and Arabic", IEEE Transaction on PAMI, Vol. 21, No. 6, pp. 495-504, 1999.
21. M. Shahrezea, K. F. Khotanzad, "Recognition of handwritten Persian/Arabic numerals by shadow coding and an edited probabilistic neural network", Proceedings of the International Conference on Image Processing, vol. 3, (1995), pp. 436-439.
22. H. Hosseini, A. Bouzerdoum, "A combined method for Persian and Arabic handwritten digit recognition", Proceedings of the Australian New Zealand Conference on Intelligent Information Systems. (1996), pp. 80-83.
23. F. Said, R. Yacoub, C. Suen,"Recognition of English and Arabic numerals using a dynamic number of hidden neurons", Proceedings of the Fifth International Conference on Document Analysis and Recognition, (1999), pp. 237-240.
24. J. Sadri, C.Y. Suen, T.D. Bui, "Application of support vector machines for recognition of handwritten Arabic/Persian digits", Proceedings of Second Iranian Conference on Machine Vision and Image Processing, vol. 1, (2003), pp. 300-307.
25. H. Soltanzadeh, M. Rahmati, "Recognition of Persian handwritten digits using image profiles of multiple orientations", Pattern Recognition Letters 25 (2004) 1569-1576.
26. V. Vapnik, Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag, 1982.
27. V. Vapnik , The Nature of Statistical Learning Theory, Springer, N.Y. 1995.
28. B. E. Boser, I. M. Guyon, and V. N. Vapnik,"A training algorithm for optimal margin classifiers", In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144 -152 Pittsburgh, PA, 1992. ACM Press.
29. C. Cortes and V. Vapnik,"Support vector networks", M. Learning, 20:273-297, 1995.
30. B. Scholkopf, C. Burges, and V. Vapnik,"Extracting support data for a given task", In U. M. Fayyad and R. Uthurusamy, editors, Proceedings, First International Conference on Knowledge Discovery&Data Mining Menlo Park, 1995. AAAI Press.
31. B. Scholkopf, C. Burges, and V. Vapnik,"Incorporating invariances in support vector learning machines", In C. von der Malsburg, W. von Seelen, J. C. Vorbruggen, and B. Sendho, editors, Artificial Neural Networks -ICANN '96, pp. 47 - 52, Berlin, 1996. Springer Lecture Notes in Computer in Computer Science, Vol. 1112.
32. B. Schölkopf, P. Simard, A. Smola, and V. Vapnik,"Prior knowledge in support vector kernels", In: Jordan M.I., Kearns M.J., and Solla S.A. (Eds.) Advances in Neural Information Processing Systems 10, MIT Press. Cambridge, MA, (1998), pp. 640-646.
33. F. Camastra, "A SVM-based cursive character recognizer", Pattern Recognition 40, (2007), pp. 3721 - 3727.
34. S. Mozaffari, K. Faez, and M. Ziaratban, "Structural decomposition and statistical description of Farsi/Arabic handwritten numeric characters," Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on, 2005, pp. 237-241 Vol. 1.
35. H. Soltanzadeh and M. Rahmati, "Recognition of Persian handwritten digits using image profiles of multiple orientations," Pattern Recognition Letters, vol. 25, Oct. 2004, pp. 1569-1576.
36. S. Mozaffari, K. Faez, and H. Kanan, "Feature comparison between fractal codes and wavelet transform in handwritten alphanumeric recognition using SVM classifier," Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2004, pp. 331-334 Vol.2.
37. A. Mowlaei and K. Faez, "Recognition of isolated handwritten Persian/Arabic characters and numerals using support vector machines," Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on, 2003, pp. 547-554.
38. V. Vapnik, Statistical Learning Theory. John Wiley and Sons, New York, 1998.
39. G. Huang, Q. Zhu, C. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), 25-29 July, 2004, Budapest, Hungary.
40. G. Huang, Q. Zhu, C. Siew, "Extreme learning machine: Theory and applications", Neurocomputing 70, 2006, pp. 489-501.
41. G. Huang, Q. Zhu, K. Mao, C. Siew, P. Saratchandran, N. Sundararajan, "Can threshold networks be trained directly?" , IEEE Trans. Circuits Syst. II 53 (3) (2006b) 187-191.
42. S. Mahmoud, http://faculty.kfupm.edu.sa/ICS/smasaad/Arabic%20Computing. htm
43. HTK Speech Recognition Toolkit, http://htk.eng.cam.ac.uk/
44. C. Hsu, C. Chang, "A Practical Guide to Support Vector Classification", www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.