A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments
In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
[1] J. Mantas, "An Overview of Character Recognition Methodologies",
Pattern Recognition, Vol.19, No.6, 1986, pp.425–430.
[2] S. Mori, C. Y. Suen and K. Yamamoto, “Historical Review of OCR
Research and Development”, Proceedings of the IEEE, Vol. 80, No. 7,
1992, pp. 1029-1058.
[3] A. L. Koerich, R. Sabourin, C. Y. Suen & A. El-Yacoubi, "A Syntax
Directed Level Building Algorithm for Large Vocabulary Handwritten
Word Recognition", In 4th International Workshop on Document
Analysis Systems (DAS 2000) , Rio de Janeiro, Brazil, December 2000.
[4] L. S. Oliveira, R. Sabourin, F. Bortolozzi and C. Y. Suen, "A Modular
System to Recognize Numerical Amounts on Brazilian Bank checks",
6th International Conference on Document Analysis and Recognition
(ICDAR 2001), Seattle-USA, IEEE Computer Society Press, September
10-13, 2001. pp 389-394.
[5] S. Ouchtati, M. Bedda, F. Bouchareb and A. Lachouri “An Off Line
System for the Handwritten Numeric Chains Recognition", International
Journal of Soft Computing (IJSC), ISSN: 1682-9503, Vol. 1, No. 4,
2006, pp. 279-287.
[6] A. Filatov, N. Nikitin, A. Volgunin, and P. Zelinsky, "The Address
Script TM Recognition System for Handwritten Envelopes", In
International Association for Pattern Recognition Workshop on
Document Analysis Systems (DAS’98), Nagano, Japan, November 4-6
1998, pp. 157–171.
[7] A. El-Yacoubi, "Modélisation Markovienne de L’´écriture Manuscrite
Application `a la Reconnaissance des Adresses Postales", PhD thesis,
Université de Rennes 1, Rennes, France, 1996.
[8] J. Hu, M. K. Brown and W Turin, “HMM based on-line handwriting
recognition”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 18, October . 1996, pp. 1039-1045.
[9] G. Kim and V. Govindaraju, “A lexicon driven approach to handwritten
word recognition for real-time applications”, IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 19, April 1997, pp. 366-
379.
[10] R. Buse, Z-Q Liu, and T. Caelli, “A structural and Relational Approach
to Handwritten Word Recognition”, IEEE Trans. Systems, Man and
Cybernetics, Part-B, Vol. 27, October. 1997, pp. 847-861.
[11] K. Liu, Y. S. Huang and C. Y. Suen, “Identification of fork Points on the
Skeletons of Handwritten Chinese Characters,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 21, October. 1999, pp.
1095-1100.
[12] A. Amin, "Off-Line Arabic Character Recognition – The State of the Art
(Review)", Pattern Recognition, vol. 31, No. 5, 1998, pp. 517-530.
[13] M. Redjimi, S. Ouchtati and M. Bedda, “A New off Line System for the
Recognition of the Isolated Handwritten Arabic Characters", Asian
Journal of Information Technology (AJIT), ISSN: 1682-3915, Vol. 5,
No. 8, 2006, pp. 912-918.
[14] S. Ouchtati, L. Bennacer and M. Bedda, “An Off Line System for the
Arabic Handwritten Words Recognition ", International Review on
Computers and Software (I.RE.CO.S.), ISSN: 1828-6003, Vol. 3, No. 6,
2008, pp. 579-585.
[15] J. Cai and Z-Q Liu, “Integration of Structural and Statistical Information
for Unconstrained Handwritten Numeral Recognition”, IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 21,
March 1999, pp. 263-270.
[16] Oivid Due Trier, Anil K. Jain, & Torfinn Taxt, “Feature Extraction
Methods for Character Recognition – A Survey”, Pattern recognition,
Vol. 29, No. 4, 1996, pp.641-662.
[17] M. Bedda, M. Ramdani et S. Ouchtati., “Sur le Choix d’une
Représentation des Caractères Manuscrits Arabes”, Proceeding du 2ème
Conférence Internationale Signaux, Systèmes, et Automatique SSA2’99,
Université de Blida, Algérie, 10-12 Mai 1999, pp73-84.
[18] F. Grandidier, "Un nouvel Algorithme de Sélection de Caractéristiques
Application à la Lecture Automatique de L’écriture manuscrite", thèse
de doctorat en génie PH.D, Ecole de Technologie Supérieure, Université
du Quèbec Canada, Janvier 2003.
[19] N. Benahmed, “Optimisation des Réseaux de Neurones Pour la
Reconnaissance des Chiffres Manuscrits Isolés, Sélection et Pondération
des Primitives par Algorithmes Génétiques”, Thèse pour l’obtention de
la Maîtrise en Génie de la Production Automatisée, Montréal le 01 Mars
2002.
[20] Salim Ouchtati, Mohammed Redjimi and Mouldi Bedda, “A Set of
Features Extraction Methods for the Recognition of the Isolated
Handwritten Digits", International Journal of Computer and
Communication Engineering (IJCCE), ISSN: 2010-3743, Vol. 3, No. 5,
September 2014, pp. 349-355.
[21] S. Ouchtati, M. Redjimi and M. Bedda, “An Offline System for the
Recognition of the Fragmented Handwritten Numeric Chains ",
International Journal of Future Computer and Communication (IJFCC),
ISSN: 2010-3751, Vol. 4, No. 1, February 2015, pp. 33-39.
[22] S. Ouchtati, M. Bedda, and A. Lachouri “Segmentation and Recognition
of Handwritten Numeric Chains", Journal of Computer Science (JCS),
ISSN: 1549-3636, Vol. 3, No. 4, 2007, pp. 242-248.
[1] J. Mantas, "An Overview of Character Recognition Methodologies",
Pattern Recognition, Vol.19, No.6, 1986, pp.425–430.
[2] S. Mori, C. Y. Suen and K. Yamamoto, “Historical Review of OCR
Research and Development”, Proceedings of the IEEE, Vol. 80, No. 7,
1992, pp. 1029-1058.
[3] A. L. Koerich, R. Sabourin, C. Y. Suen & A. El-Yacoubi, "A Syntax
Directed Level Building Algorithm for Large Vocabulary Handwritten
Word Recognition", In 4th International Workshop on Document
Analysis Systems (DAS 2000) , Rio de Janeiro, Brazil, December 2000.
[4] L. S. Oliveira, R. Sabourin, F. Bortolozzi and C. Y. Suen, "A Modular
System to Recognize Numerical Amounts on Brazilian Bank checks",
6th International Conference on Document Analysis and Recognition
(ICDAR 2001), Seattle-USA, IEEE Computer Society Press, September
10-13, 2001. pp 389-394.
[5] S. Ouchtati, M. Bedda, F. Bouchareb and A. Lachouri “An Off Line
System for the Handwritten Numeric Chains Recognition", International
Journal of Soft Computing (IJSC), ISSN: 1682-9503, Vol. 1, No. 4,
2006, pp. 279-287.
[6] A. Filatov, N. Nikitin, A. Volgunin, and P. Zelinsky, "The Address
Script TM Recognition System for Handwritten Envelopes", In
International Association for Pattern Recognition Workshop on
Document Analysis Systems (DAS’98), Nagano, Japan, November 4-6
1998, pp. 157–171.
[7] A. El-Yacoubi, "Modélisation Markovienne de L’´écriture Manuscrite
Application `a la Reconnaissance des Adresses Postales", PhD thesis,
Université de Rennes 1, Rennes, France, 1996.
[8] J. Hu, M. K. Brown and W Turin, “HMM based on-line handwriting
recognition”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 18, October . 1996, pp. 1039-1045.
[9] G. Kim and V. Govindaraju, “A lexicon driven approach to handwritten
word recognition for real-time applications”, IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 19, April 1997, pp. 366-
379.
[10] R. Buse, Z-Q Liu, and T. Caelli, “A structural and Relational Approach
to Handwritten Word Recognition”, IEEE Trans. Systems, Man and
Cybernetics, Part-B, Vol. 27, October. 1997, pp. 847-861.
[11] K. Liu, Y. S. Huang and C. Y. Suen, “Identification of fork Points on the
Skeletons of Handwritten Chinese Characters,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 21, October. 1999, pp.
1095-1100.
[12] A. Amin, "Off-Line Arabic Character Recognition – The State of the Art
(Review)", Pattern Recognition, vol. 31, No. 5, 1998, pp. 517-530.
[13] M. Redjimi, S. Ouchtati and M. Bedda, “A New off Line System for the
Recognition of the Isolated Handwritten Arabic Characters", Asian
Journal of Information Technology (AJIT), ISSN: 1682-3915, Vol. 5,
No. 8, 2006, pp. 912-918.
[14] S. Ouchtati, L. Bennacer and M. Bedda, “An Off Line System for the
Arabic Handwritten Words Recognition ", International Review on
Computers and Software (I.RE.CO.S.), ISSN: 1828-6003, Vol. 3, No. 6,
2008, pp. 579-585.
[15] J. Cai and Z-Q Liu, “Integration of Structural and Statistical Information
for Unconstrained Handwritten Numeral Recognition”, IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 21,
March 1999, pp. 263-270.
[16] Oivid Due Trier, Anil K. Jain, & Torfinn Taxt, “Feature Extraction
Methods for Character Recognition – A Survey”, Pattern recognition,
Vol. 29, No. 4, 1996, pp.641-662.
[17] M. Bedda, M. Ramdani et S. Ouchtati., “Sur le Choix d’une
Représentation des Caractères Manuscrits Arabes”, Proceeding du 2ème
Conférence Internationale Signaux, Systèmes, et Automatique SSA2’99,
Université de Blida, Algérie, 10-12 Mai 1999, pp73-84.
[18] F. Grandidier, "Un nouvel Algorithme de Sélection de Caractéristiques
Application à la Lecture Automatique de L’écriture manuscrite", thèse
de doctorat en génie PH.D, Ecole de Technologie Supérieure, Université
du Quèbec Canada, Janvier 2003.
[19] N. Benahmed, “Optimisation des Réseaux de Neurones Pour la
Reconnaissance des Chiffres Manuscrits Isolés, Sélection et Pondération
des Primitives par Algorithmes Génétiques”, Thèse pour l’obtention de
la Maîtrise en Génie de la Production Automatisée, Montréal le 01 Mars
2002.
[20] Salim Ouchtati, Mohammed Redjimi and Mouldi Bedda, “A Set of
Features Extraction Methods for the Recognition of the Isolated
Handwritten Digits", International Journal of Computer and
Communication Engineering (IJCCE), ISSN: 2010-3743, Vol. 3, No. 5,
September 2014, pp. 349-355.
[21] S. Ouchtati, M. Redjimi and M. Bedda, “An Offline System for the
Recognition of the Fragmented Handwritten Numeric Chains ",
International Journal of Future Computer and Communication (IJFCC),
ISSN: 2010-3751, Vol. 4, No. 1, February 2015, pp. 33-39.
[22] S. Ouchtati, M. Bedda, and A. Lachouri “Segmentation and Recognition
of Handwritten Numeric Chains", Journal of Computer Science (JCS),
ISSN: 1549-3636, Vol. 3, No. 4, 2007, pp. 242-248.
@article{"International Journal of Information, Control and Computer Sciences:71216", author = "Salim Ouchtati and Jean Sequeira and Mouldi Bedda", title = "A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments", abstract = "In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.", keywords = "Handwritten word recognition, neural networks,
image processing, pattern recognition, features extraction.", volume = "9", number = "8", pages = "1984-7", }
