A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks
This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.
[1] I.S. Abuhaiba and P. Ahmed, "Restoration of Temporal Information in
Off-Line Arabic Handwriting," Pattern Recognition(26), No. 7, July
1993, pp. 1009-1017.
[2] M. Altuwaijri and A. Bayoumi, "A new recognition system for multifont
Arabic Cursive words, " Proceedings of ICECS'95, Amman-
Jordan, pp.298-303,1995.
[3] A. Amin, H.B. Al-Sadoun and S. Fischer, "Hand-Printed Arabic
Character-Recognition System Using an Artificial Network," Pattern
Recognition(29), No. 4, April 1996, pp. 663-675.
[4] J. Cao, M. Ahmadi and M. Shridhar, "Recognition of handwritten
numerals with multiple feature and multistage classifier," Pattern
Recognition,1995, vol.28, no.2, pp. 153-160.
[5] S. G├╝nter and H. Bunke, "Multiple classifier systems in off-line
handwritten word recognition - on the influence of training set and
vocabulary size," Int. Journal of Pattern Recognition and Art.
Intelligence, 2004, vol. 18, no. 7, pages 1303 - 1320.
[6] F. Kimura and M. Shridhar, "Handwritten numerical recognition based
on multiple algorithms," Pattern Recognition,1991, vol.24,
no.lO.pp.969-983.
[7] Z. Shaaban and G. Sulong, "Uppercase hand-printed character
recognition using parallel neural architecture," Proc. of the 1ASTED
International conference modeling and simulation Pittsburgh-USA,
1995 pp.307-309
[8] Z. Shaaban and Z. Sulong, "Recognition of connected handwritten
characters based on moments invariants using neural networks,"
Proceedings of ACCV'95 Second Asian Conference on Computer
Vision, 1995, Singapore pp.l- 335-339
[9] Z. Shaaban, G. Sulong and B. Duin, "Recognition of handprinted
characters using distance transform and moment invariants via parallel
neural networks," Proceedings of ICECS'95 International conference on
electronics, circuits and systems '95, 1995, Amman-Jordan, pp.393-
398.
[10] Z. Shaaban, G. Sulong and B. Duin, "Symbol recognition based on
distance transform," Proc. of the IASTED International conference on
signal and image processing and applications, 1996 Annecy-France,
pp.225-230.
[11] S. N. Srihari, "Recognition of handwritten and machine printed text of
postal address interpretation," In (4).
[12] C. Y. Suen, "Computer recognition of unconstrained handwritten
numerals," Proceedings of the IEEE vol.80, no.7, pp.1162-1180,1992.
[13] L. Xu, A. Krzyzak and C. Y. Suen, "Methods of combining multiple
classifiers and their applications to handwriting recognition," IEEE
transactions on systems, man, and Cybernetics vol.22, no.3, pp.418-
435,1992.
[14] Z. Shaaban, Algorithms for off-line upper-case hand-written text
recognition and its associated processes, Ph.D. Thesis, University of
Technology Malaysia, 1996.
[1] I.S. Abuhaiba and P. Ahmed, "Restoration of Temporal Information in
Off-Line Arabic Handwriting," Pattern Recognition(26), No. 7, July
1993, pp. 1009-1017.
[2] M. Altuwaijri and A. Bayoumi, "A new recognition system for multifont
Arabic Cursive words, " Proceedings of ICECS'95, Amman-
Jordan, pp.298-303,1995.
[3] A. Amin, H.B. Al-Sadoun and S. Fischer, "Hand-Printed Arabic
Character-Recognition System Using an Artificial Network," Pattern
Recognition(29), No. 4, April 1996, pp. 663-675.
[4] J. Cao, M. Ahmadi and M. Shridhar, "Recognition of handwritten
numerals with multiple feature and multistage classifier," Pattern
Recognition,1995, vol.28, no.2, pp. 153-160.
[5] S. G├╝nter and H. Bunke, "Multiple classifier systems in off-line
handwritten word recognition - on the influence of training set and
vocabulary size," Int. Journal of Pattern Recognition and Art.
Intelligence, 2004, vol. 18, no. 7, pages 1303 - 1320.
[6] F. Kimura and M. Shridhar, "Handwritten numerical recognition based
on multiple algorithms," Pattern Recognition,1991, vol.24,
no.lO.pp.969-983.
[7] Z. Shaaban and G. Sulong, "Uppercase hand-printed character
recognition using parallel neural architecture," Proc. of the 1ASTED
International conference modeling and simulation Pittsburgh-USA,
1995 pp.307-309
[8] Z. Shaaban and Z. Sulong, "Recognition of connected handwritten
characters based on moments invariants using neural networks,"
Proceedings of ACCV'95 Second Asian Conference on Computer
Vision, 1995, Singapore pp.l- 335-339
[9] Z. Shaaban, G. Sulong and B. Duin, "Recognition of handprinted
characters using distance transform and moment invariants via parallel
neural networks," Proceedings of ICECS'95 International conference on
electronics, circuits and systems '95, 1995, Amman-Jordan, pp.393-
398.
[10] Z. Shaaban, G. Sulong and B. Duin, "Symbol recognition based on
distance transform," Proc. of the IASTED International conference on
signal and image processing and applications, 1996 Annecy-France,
pp.225-230.
[11] S. N. Srihari, "Recognition of handwritten and machine printed text of
postal address interpretation," In (4).
[12] C. Y. Suen, "Computer recognition of unconstrained handwritten
numerals," Proceedings of the IEEE vol.80, no.7, pp.1162-1180,1992.
[13] L. Xu, A. Krzyzak and C. Y. Suen, "Methods of combining multiple
classifiers and their applications to handwriting recognition," IEEE
transactions on systems, man, and Cybernetics vol.22, no.3, pp.418-
435,1992.
[14] Z. Shaaban, Algorithms for off-line upper-case hand-written text
recognition and its associated processes, Ph.D. Thesis, University of
Technology Malaysia, 1996.
@article{"International Journal of Information, Control and Computer Sciences:53025", author = "Z. Shaaban", title = "A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks", abstract = "This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.
", keywords = "Neural Networks, character recognition, feature
extraction, multiple networks, Arabic text.", volume = "2", number = "5", pages = "1444-4", }
