On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net
On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
[1] Alessandro Vinciarelli (2002). A survey on off-line Cursive
Word Recognition. Pattern Recognition, 35: 1433.
[2] Ahmed, S.M.,et.al, (Nov.1995). Experiments in character
recognition to develop tools for an optical character
recognition system, IEEE Inc. 1st National Multi Topic Conf.
proc. NUST, Rawalpindi, Pakistan, 61-67.
[3] Alexandre Lemieux, Christian Gagne and Marc Parizeau.
(2002). Genetical Engineering of Handwriting Representations.
Proc. of the International Workshop on Frontiers in
Handwriting Recognition (IWFHR), Niagara-On-The-Lake,
August 6-8.
[4] Anoop M. Namboodiri, Anil K. Jain. (2004). Online
Handwritten Script Recognition. IEEE Trans. PAMI. 26(1):
124-130.
[5] Bishop M. (1995). Neural Networks for Pattern Recognition.
Oxford Univ. Press, Oxford-U.K.
[6] Bortolozzi, F., Britto Jr,. A., Oliveira, L. S. and Morita, M.,
(2005). Recent Advances in Handwriting Recognition. In
Umapada Pal et al editors, Document Analysis, 1-31.
[7] Dzulkifli Mohamad, and Zafar, M. F., (2004a). Recognition of
Complex Patterns Using a Novel Feature Vector by
Backpropagation Neural-Network. Pattern Recognition And
Image Analysis. 14(3): 479-487.
[8] Dzulkifli Mohamad, and Zafar, M. F., (2004b). Comparative
Study of Two Novel Feature Vectors For Complex Image
Matching Using Counterpropagation Neural Network. Journal
of Information Technology, vol.16, No.1, FSKSM, UTM.
[9] Freeman, J. A., and Skapura, D. M., (1991). Neural Networks:
Algrithms, Applications and Programming Tecniques, Addison-
Wesley Publishing Company.
[10]Gader P. D., Forester B., Ganzberger M., A. Billies, B.
Mitchell, M. Whalen, T. Youcum (1991). Recognition of
handwritten digits using template and model matching. Pattern
Recognition, 5(24):421-431.
[11]Govindan, V. K.. and Shivaprasad, A. P. (1990), Character
Recognition-a Review, Pattern Recognition, 23: 671-683.
[12]Hebert Jean-Francois, Parizeau Marc and Nadia Ghazali.
(1998). A new fuzzy geometric representation for on-line
isolated character recognition. Proc. of the 14th International
Conference on Pattern Recognition, Brisbane: 1121-1123.
[13]Hecht-Nielsen, R. (1987). Counterpropagation networks. Proc.
Of IEEE First Int-l Conference on Neural Networks. 1987 II:
19-32.
[14]Hecht-Nielsen Robert.(1990). Neurocomputing. Addison-
Wesley Publishing Company.
[15]Hiroto Mitoma, Seiichi Uchida, and Hiroaki Sakoe. (2005).
Online character recognition based on elastic matching and
quadratic discrimination. Proceedings of 8th International
Conference on Document Analysis and Recognition (ICDAR
2005, Seoul, Korea).1(2):36ÔÇö40.
[16]Iqbal, A., Zafar, M. Faisal., (1998). Pc Based Optical
Objective Test Marking System Using Neural Networks.
International Workshop on Recent Advances in Computer
Vision proc. SZABIST, Karachi, Pakistan,: 41-50.
[17]Kapp, M.N., Freitas, C. and Sabourin, R. (2004).Handwritten
Brazilian Month Recognition: An Analysis of Two NN
Architectures and a Rejection Mechanism. 9th International
Workshop on Frontiers in Handwriting Recognition (IWFHR-
9), Tokyo, Japan,
[18]Koerich L. (2002). Large Vocabulary Off-Line Handwritten
Word Recognition. PhD thesis, École de Technologie
Supérieure, Montreal-Canada.
[19]Koerich A. L., Sabourin R., Suen C. Y. (2003). Large
vocabulary off-line handwriting recognition:A survey. Pattern
Anal Applic, 6: 97-121.
[20]LeCun Y., Bottou L., Orr G. B., Muller K. R. (1998a). Eficient
backprop. In Orr G. and K. Miller, editors, Neural Networks:
Tricks of the Trade. Springer.
[21]LeCun Y., Bottou L., Bengio Y., and Haffner P. (1998b).
Gradient-Based Learning Applied to Document Recognition.
Proc. IEEE, 86(11): 2278-2324.
[22]Lethelier, Leroux M., and Gilloux M. (1995). An automatic
reading system for handwritten numeral amounts on french
checks. In Proc. 3th International Conference on Document
Analysis and Recognition, Montreal-Canada, pp. pages 92-97.
[23]Ling, M. Lizaraga, N. Gomes, and A. Koerich (1997). A
prototype for brazilian bankcheck recognition. In S.Impedovo
et al, editor, International Journal of Pattern Recognition and
Artificial Intelligence,World Scientific, pp. 549-569.
[24]Liu Cheng-Lin, Stefan Jaeger, and Masaki Nakagawa (2004).
Online Recognition of Chinese Characters:The State-of-the-
Art. IEEE Trans. on Pattern Analysis and Machine Intelligence,
26(2), 198-203.
[25]Mantas, J. (1986), An Overview of Character Recognition
Methodologies, Pattern Recognition, 19 (1986) 425-430.
[26]Matan O., Burges J. C., LeCun Y., Denker J. S. (1992). Multidigit
recognition using a space displacement neural network. In
J. E. Moody, S. J. Hanson, and 165 R. L. Lippmann, editors,
Advances in Neural Information Processing Systems, volume 4,
Morgan Kaufmann, pp. 488-495.
[27]Morita M., Sabourin R., Bortolozzi F., Suen C. Y. (2003). A
Recognition and Verification Strategy for Handwritten Word
Recognition. ICDAR'03), Edinburgh-Scotland: 482-486.
[28]Nouboud, F., and Plamondon,(1990). On-Line Recognition of
Handprinted Chara.cters: Survey and Beta Tests, Pattern
Recognition, 23: 1031-1044.
[29]Plamondon Rejean, and Sargur N. Srihari, (2000). On-Line and
Off-Line Handwriting Recognition: A Comprehensive Survey.
1EEE Transactions on PAMI. 22(1): 63-84.
[30]Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986).
Learning internal representations by error propagation. in
Rumelhart, D. E. And McClelland, J. L. [Ed], Parallel
Distributed Processing: Explorations in the Microstructure of
Cognition, 1, 318-362, MIT Press, Cambrige.
[31]Shridhar M. and Badreldin A. (1986). Recognition of isolated
and simply connected handwritten numerals. Pattern
Recognition, 19(1):1-12.
[32]Suen, C. Y., Berthod, M., and Mori, S. (1980), Automatic
Recognition of Ha.nd printed Character-the State of the Art,
Proceedings of IEEE. 68 (1980) 469-487.
[33]Steinherz T., Rivlin E., and Intrator N. (1999). Offline Cursive
Script Word RecognitionÔÇöA Survey. Int-l J. Document
Analysis and Recognition, vol. 2, 90-110.
[34]Tappert C.C., Suen C.Y., Wakahara T.(1990). The state of the
art in on-line handwriting recognition. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 12(8), 787-808.
[35]Zafar, M. F., and Dzulkifli Mohamad. (2005). Comparison of
Two Different Proposed Feature Vectors For Classification of
Complex Image. Jurnal Teknologi, Universiti Teknologi
Malaysia, 42(D): 65-82.
[36]Zafar, M. F., and Dzulkifli Mohamad. (2002). A Comparison
Of Two Neural Network Techniques On Feature Based
Complex Image Recognition. 2nd World Engineering Congress,
WEC2002, Kuching, Sarawak, Malaysia.
[37]Zhang G. P. (2000). Neural networks for classification: a
survey. IEEE Transactions on Systems, Man, and Cybernetics -
Part C: Applications and Reviews, 30(4):451-462.
[38]Zhang, Fu M., Yan H., and Fabri M. A. (1999). Handwritten
digit recognition by adaptativesubspace self organizing map.
IEEE Trans. on Neural Networks, 10:939-945.
[39]Zhou J (1999). Recognition and Verification of Unconstrained
Handwritten Numeral. PhD thesis, Concordia University,
Montreal-Canada.
[1] Alessandro Vinciarelli (2002). A survey on off-line Cursive
Word Recognition. Pattern Recognition, 35: 1433.
[2] Ahmed, S.M.,et.al, (Nov.1995). Experiments in character
recognition to develop tools for an optical character
recognition system, IEEE Inc. 1st National Multi Topic Conf.
proc. NUST, Rawalpindi, Pakistan, 61-67.
[3] Alexandre Lemieux, Christian Gagne and Marc Parizeau.
(2002). Genetical Engineering of Handwriting Representations.
Proc. of the International Workshop on Frontiers in
Handwriting Recognition (IWFHR), Niagara-On-The-Lake,
August 6-8.
[4] Anoop M. Namboodiri, Anil K. Jain. (2004). Online
Handwritten Script Recognition. IEEE Trans. PAMI. 26(1):
124-130.
[5] Bishop M. (1995). Neural Networks for Pattern Recognition.
Oxford Univ. Press, Oxford-U.K.
[6] Bortolozzi, F., Britto Jr,. A., Oliveira, L. S. and Morita, M.,
(2005). Recent Advances in Handwriting Recognition. In
Umapada Pal et al editors, Document Analysis, 1-31.
[7] Dzulkifli Mohamad, and Zafar, M. F., (2004a). Recognition of
Complex Patterns Using a Novel Feature Vector by
Backpropagation Neural-Network. Pattern Recognition And
Image Analysis. 14(3): 479-487.
[8] Dzulkifli Mohamad, and Zafar, M. F., (2004b). Comparative
Study of Two Novel Feature Vectors For Complex Image
Matching Using Counterpropagation Neural Network. Journal
of Information Technology, vol.16, No.1, FSKSM, UTM.
[9] Freeman, J. A., and Skapura, D. M., (1991). Neural Networks:
Algrithms, Applications and Programming Tecniques, Addison-
Wesley Publishing Company.
[10]Gader P. D., Forester B., Ganzberger M., A. Billies, B.
Mitchell, M. Whalen, T. Youcum (1991). Recognition of
handwritten digits using template and model matching. Pattern
Recognition, 5(24):421-431.
[11]Govindan, V. K.. and Shivaprasad, A. P. (1990), Character
Recognition-a Review, Pattern Recognition, 23: 671-683.
[12]Hebert Jean-Francois, Parizeau Marc and Nadia Ghazali.
(1998). A new fuzzy geometric representation for on-line
isolated character recognition. Proc. of the 14th International
Conference on Pattern Recognition, Brisbane: 1121-1123.
[13]Hecht-Nielsen, R. (1987). Counterpropagation networks. Proc.
Of IEEE First Int-l Conference on Neural Networks. 1987 II:
19-32.
[14]Hecht-Nielsen Robert.(1990). Neurocomputing. Addison-
Wesley Publishing Company.
[15]Hiroto Mitoma, Seiichi Uchida, and Hiroaki Sakoe. (2005).
Online character recognition based on elastic matching and
quadratic discrimination. Proceedings of 8th International
Conference on Document Analysis and Recognition (ICDAR
2005, Seoul, Korea).1(2):36ÔÇö40.
[16]Iqbal, A., Zafar, M. Faisal., (1998). Pc Based Optical
Objective Test Marking System Using Neural Networks.
International Workshop on Recent Advances in Computer
Vision proc. SZABIST, Karachi, Pakistan,: 41-50.
[17]Kapp, M.N., Freitas, C. and Sabourin, R. (2004).Handwritten
Brazilian Month Recognition: An Analysis of Two NN
Architectures and a Rejection Mechanism. 9th International
Workshop on Frontiers in Handwriting Recognition (IWFHR-
9), Tokyo, Japan,
[18]Koerich L. (2002). Large Vocabulary Off-Line Handwritten
Word Recognition. PhD thesis, École de Technologie
Supérieure, Montreal-Canada.
[19]Koerich A. L., Sabourin R., Suen C. Y. (2003). Large
vocabulary off-line handwriting recognition:A survey. Pattern
Anal Applic, 6: 97-121.
[20]LeCun Y., Bottou L., Orr G. B., Muller K. R. (1998a). Eficient
backprop. In Orr G. and K. Miller, editors, Neural Networks:
Tricks of the Trade. Springer.
[21]LeCun Y., Bottou L., Bengio Y., and Haffner P. (1998b).
Gradient-Based Learning Applied to Document Recognition.
Proc. IEEE, 86(11): 2278-2324.
[22]Lethelier, Leroux M., and Gilloux M. (1995). An automatic
reading system for handwritten numeral amounts on french
checks. In Proc. 3th International Conference on Document
Analysis and Recognition, Montreal-Canada, pp. pages 92-97.
[23]Ling, M. Lizaraga, N. Gomes, and A. Koerich (1997). A
prototype for brazilian bankcheck recognition. In S.Impedovo
et al, editor, International Journal of Pattern Recognition and
Artificial Intelligence,World Scientific, pp. 549-569.
[24]Liu Cheng-Lin, Stefan Jaeger, and Masaki Nakagawa (2004).
Online Recognition of Chinese Characters:The State-of-the-
Art. IEEE Trans. on Pattern Analysis and Machine Intelligence,
26(2), 198-203.
[25]Mantas, J. (1986), An Overview of Character Recognition
Methodologies, Pattern Recognition, 19 (1986) 425-430.
[26]Matan O., Burges J. C., LeCun Y., Denker J. S. (1992). Multidigit
recognition using a space displacement neural network. In
J. E. Moody, S. J. Hanson, and 165 R. L. Lippmann, editors,
Advances in Neural Information Processing Systems, volume 4,
Morgan Kaufmann, pp. 488-495.
[27]Morita M., Sabourin R., Bortolozzi F., Suen C. Y. (2003). A
Recognition and Verification Strategy for Handwritten Word
Recognition. ICDAR'03), Edinburgh-Scotland: 482-486.
[28]Nouboud, F., and Plamondon,(1990). On-Line Recognition of
Handprinted Chara.cters: Survey and Beta Tests, Pattern
Recognition, 23: 1031-1044.
[29]Plamondon Rejean, and Sargur N. Srihari, (2000). On-Line and
Off-Line Handwriting Recognition: A Comprehensive Survey.
1EEE Transactions on PAMI. 22(1): 63-84.
[30]Rumelhart, D. E., Hinton, G. E., and Williams, R. J., (1986).
Learning internal representations by error propagation. in
Rumelhart, D. E. And McClelland, J. L. [Ed], Parallel
Distributed Processing: Explorations in the Microstructure of
Cognition, 1, 318-362, MIT Press, Cambrige.
[31]Shridhar M. and Badreldin A. (1986). Recognition of isolated
and simply connected handwritten numerals. Pattern
Recognition, 19(1):1-12.
[32]Suen, C. Y., Berthod, M., and Mori, S. (1980), Automatic
Recognition of Ha.nd printed Character-the State of the Art,
Proceedings of IEEE. 68 (1980) 469-487.
[33]Steinherz T., Rivlin E., and Intrator N. (1999). Offline Cursive
Script Word RecognitionÔÇöA Survey. Int-l J. Document
Analysis and Recognition, vol. 2, 90-110.
[34]Tappert C.C., Suen C.Y., Wakahara T.(1990). The state of the
art in on-line handwriting recognition. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 12(8), 787-808.
[35]Zafar, M. F., and Dzulkifli Mohamad. (2005). Comparison of
Two Different Proposed Feature Vectors For Classification of
Complex Image. Jurnal Teknologi, Universiti Teknologi
Malaysia, 42(D): 65-82.
[36]Zafar, M. F., and Dzulkifli Mohamad. (2002). A Comparison
Of Two Neural Network Techniques On Feature Based
Complex Image Recognition. 2nd World Engineering Congress,
WEC2002, Kuching, Sarawak, Malaysia.
[37]Zhang G. P. (2000). Neural networks for classification: a
survey. IEEE Transactions on Systems, Man, and Cybernetics -
Part C: Applications and Reviews, 30(4):451-462.
[38]Zhang, Fu M., Yan H., and Fabri M. A. (1999). Handwritten
digit recognition by adaptativesubspace self organizing map.
IEEE Trans. on Neural Networks, 10:939-945.
[39]Zhou J (1999). Recognition and Verification of Unconstrained
Handwritten Numeral. PhD thesis, Concordia University,
Montreal-Canada.
@article{"International Journal of Information, Control and Computer Sciences:50451", author = "Muhammad Faisal Zafar and Dzulkifli Mohamad and Razib M. Othman", title = "On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net", abstract = "On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
", keywords = "On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates.", volume = "1", number = "10", pages = "2935-6", }
