Deductive method for recognition of on-line handwritten Persian/Arabic characters

2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010

Volumn 5, Issue , 2010, Pages 791-795

  Harouni, Majid ^a   Mohamad, Dzulkifli ^a   Rasouli, Abdolreza ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Neural network; Online character recognition; Persian and Arabic scripts; Pre processing

Indexed keywords

ARABIC SCRIPTS; ONLINE CHARACTER RECOGNITION; PERSIANS; PRE-PROCESSING;

CLASSIFIERS; COMPUTATIONAL METHODS; EQUATIONS OF STATE; FEATURE EXTRACTION; FEEDFORWARD NEURAL NETWORKS;

CHARACTER RECOGNITION;

EID: 77952651012     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCAE.2010.5451869     Document Type: Conference Paper

References (16)

1. M. Dehghan and K. Faez (1997). Farsi Handwritten Character Recognition with Moment Invariants. 13th IEEE International Conference on Digital Signal Processing DSP 97. 2. 507-510.
2. N. Mezghani, A. Mitiche, and M. cheriet. On-line recognition of handwritten arabic characters using a kohonen neural network. In Proc. 8th International workshop on frontiers in handwriting recognition : 'IWFHR'02, pages 490{495, Niagara-on-the-Lake, Canada, 2002.
3. L. Cheng, and Y. S. Ching, a New Benchmark on the Recognition of Handwritten Bangla and Farsi Numeral Characters. ICFHR, 2008. Montreal, Canada. Aug. 2008. pp: 278-283.
4. A. Takeshi, T. Hiroki, N. Hiroshi, (1991). Recognition of Handwritten Katakana in a Frame using Moment Invariants Based on Neural Network. IEEE International Joint Conference on Neural Network. pp. 659-664J.
5. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol.2. Oxford: Clarendon, 1892, pp.68-73.
6. Y.Y. Chung, M.T. Wong, and M. Bennamoun, (1998). Handwritten Character Recognition by Contour Sequence Moments and Neural Network. IEEE International Conference on System, Man and Cybernatics. 4184-4188.
7. M.M. Altuwaijri, M.A Bayoumi (1994)..: Arabic Text Recognition Using Neural Networks. International Symposium on Circuits and Systems (ISCAS 1994). London. IEEE: 415-418.
8. J. Shanbehzadeh, H. Pezashki, A. Sarrafzadeh, (2007), 'Features Extraction from Farsi Hand Written Letters', Proceedings of Image and Vision Computing New Zealand 2007, , Hamilton, New Zealand, December 2007. pp. 35-40.
9. J.Sadri, S.Izadi, F.Solimanpour, CYSuen,and TDBui, "State-of-theart in Farsi script recognition," in Proc. 9th Int. Symp. on Signal Processing and its Applications, IEEE, Piscataway, NJ (2007).
10. J. Sternby, J. Morwing, J. Andersson, C. Friberg, On-line Arabic handwriting recognition with templates, Pattern Recognition, Volume 42, Issue 12, New Frontiers in Handwriting Recognition, December 2009, Pages 3278-3286, ISSN 0031-3203, DOI: 10.1016/j.patcog. 2008.12.017.
11. H. Almuallim and S. Yamaguchi (1987), A method of recognition of Arabic cursive handwriting, IEEE Transactions on Pattern Analysis and Machine Intelligence. Washington, DC, USA, 1987, 715-722.
12. M.S. Khorsheed, W.F. Clocksin, (1999). Structural features of cursive Arabic script. In: The 10th British Machine Vision Conference, vol.2. University of Nottingham, UK, 1999, pp. 422- 431.
13. M. Soleymani Baghshah, S. Bagheri Shouraki, S. Kasaei (2005), A Novel Fuzzy Approach to Recognition of Online Persian Handwriting, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), 2005. pp. 268-273
14. A. Al-Taani, M. Hammad, (2005), an expert system for car failure diagnosis, International Ethological Conference or IEC (Prague), 20- 27 August 2005. Budapest, Hungary, pp. 457-560.
15. J. Sternby, "An additive single character recognition method", in: Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, 2006, pp.417-422.
16. M. Fernandez-Redondo, C. Hernandez-Espinosa, (2000), A Comparison among Weight Initialization Methods for Multilayer Feed forward Networks, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume, vol.4, pp.4543, 4.