Segmentation and Recognition of Handwritten Numeric Chains
In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case 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 digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.
[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] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] A. Amin, "Off-line Arabic character recognition - the state of the art
[review]", Pattern Recognition, vol. 31, No. 5, 1998, pp. 517-530.
[12] 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.
[13] 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.
[14] 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.
[15] F. Grandidier, "Un nouvel algorithme de sélection de caractéristiques
application ├á la lecture automatique de l-écriture manuscrite", thèse de
doctorat en génie PH.D, école de technologie supérieure, université du
Quèbec Canada, Janvier 2003.
[16] 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 Ma├«trise en Génie de la Production Automatisée,
Montréal le 01 Mars 2002.
[17] T. K. HO, J. J. Hull and S. N. Srihari. "Decision Combination in
Multiple Classifiers System", IEEE Transactions on pattern Analysis
and Machine Intelligence, Vol. 16, No. 1, 1994, pp. 66-75.
[18] Y. S. Huang and C. Y. Suen.: "A Method of Combining Multiple
Experts for the Recognition of Unconstrained Handwritten Numerals",
IEEE Transactions on pattern Analysis and Machine Intelligence, Vol.
17, No. 1, 1995, pp.90-94.
[19] S. Ouchtati M. Ramdani et M. Bedda, "Un Réseau de Neurones
Multicouches Pour la Reconnaissance Hors-Ligne des Caractères
Manuscrits Arabes", Revue Sciences et Technologie Université de
Constantine, No. 17, Juin 2002, pp. 99-105.
[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] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] A. Amin, "Off-line Arabic character recognition - the state of the art
[review]", Pattern Recognition, vol. 31, No. 5, 1998, pp. 517-530.
[12] 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.
[13] 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.
[14] 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.
[15] F. Grandidier, "Un nouvel algorithme de sélection de caractéristiques
application ├á la lecture automatique de l-écriture manuscrite", thèse de
doctorat en génie PH.D, école de technologie supérieure, université du
Quèbec Canada, Janvier 2003.
[16] 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 Ma├«trise en Génie de la Production Automatisée,
Montréal le 01 Mars 2002.
[17] T. K. HO, J. J. Hull and S. N. Srihari. "Decision Combination in
Multiple Classifiers System", IEEE Transactions on pattern Analysis
and Machine Intelligence, Vol. 16, No. 1, 1994, pp. 66-75.
[18] Y. S. Huang and C. Y. Suen.: "A Method of Combining Multiple
Experts for the Recognition of Unconstrained Handwritten Numerals",
IEEE Transactions on pattern Analysis and Machine Intelligence, Vol.
17, No. 1, 1995, pp.90-94.
[19] S. Ouchtati M. Ramdani et M. Bedda, "Un Réseau de Neurones
Multicouches Pour la Reconnaissance Hors-Ligne des Caractères
Manuscrits Arabes", Revue Sciences et Technologie Université de
Constantine, No. 17, Juin 2002, pp. 99-105.
@article{"International Journal of Information, Control and Computer Sciences:62568", author = "Salim Ouchtati and Bedda Mouldi and Abderrazak Lachouri", title = "Segmentation and Recognition of Handwritten Numeric Chains", abstract = "In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case 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 digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.", keywords = "Optical Characters Recognition, Neural networks,Barr features, Image processing, Pattern Recognition, Featuresextraction.", volume = "2", number = "12", pages = "4219-8", }
