]. T. Bayer, U. Bohnacker, and &. I. Renz, Information Extraction from Paper Documents. Handbook of Character Recognition and Document Image Analysis, BIBLIOGRAPHIE, 1997.

A. Bellili, M. Gilloux, and &. P. Gallinari, An hybrid MLP-SVM handwritten digit recognizer, Proceedings of Sixth International Conference on Document Analysis and Recognition, pp.28-32, 2001.
DOI : 10.1109/ICDAR.2001.953749

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.8957

A. Bellili, M. Gilloux, and &. P. Gallinari, An MLP-SVM combination architecture for offline handwritten digit recognition : Reduction of recognition errors by Support Vector Machines rejection mechanisms, IJDAR, vol.5, issue.4, pp.244-252, 2003.

L. Cun, C. Nohl, and &. C. Burges, LeRec : A NN- HMM hybrid for on-line handwriting recognition, Neural Computation, vol.7, issue.6, pp.1289-1303, 1995.

D. M. Bikel, R. L. Schwartz, and &. M. , An Algorithm that Learns What's in a Name, Machine Learning, pp.211-231, 1999.

R. D. Bippus, 1-dimensional and pseudo 2-dimensional HMMs for the recognition of German literal amounts, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.487-490, 1997.
DOI : 10.1109/ICDAR.1997.620546

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon et al., Comparison of classifier methods: a case study in handwritten digit recognition, Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), pp.77-82, 1994.
DOI : 10.1109/ICPR.1994.576879

R. M. Bozinovic and &. S. Srihari, Off-line cursive script word recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.1, pp.68-83, 1989.
DOI : 10.1109/34.23114

]. A. Bradley-97 and . Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, vol.30, issue.7, pp.1145-1159, 1997.
DOI : 10.1016/S0031-3203(96)00142-2

L. Breiman, J. Friedman, R. Olshen, and &. C. Stone, Classification and Regression Trees, 1984.

J. S. Bridle, Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition, Neurocomputing : Algorithms, Architectures and Applications, pp.227-236, 1990.
DOI : 10.1007/978-3-642-76153-9_28

A. Britto, R. Sabourin, E. Lethelier, F. Bortolozzi, and &. C. Suen, Improvement handwritten numeral string recognition by slant normalization and contextual information, pp.323-332, 2000.

]. A. Britto, R. Sabourin, F. Bortolozzi, and &. C. Suen, A String Length Predictor to Control the Level Building of HMMs for Handwritten Numeral Recognition, ICPR, vol.4, pp.31-34, 2002.

A. Britto, R. Sabourin, F. Bortolozzi, and &. C. Suen, Recognition of handwritten numeral strings using a two-stage HMM- Based method, IJDAR, vol.5, pp.102-117, 2003.

]. J. Bromley and &. J. Denker, Improving Rejection Performance on Handwritten Digits by Training with ???Rubbish???, Neural Computation, vol.3, issue.3, pp.367-370, 1993.
DOI : 10.1109/2.144441

L. T. Bui, D. Essam, H. A. Abbass, and &. D. Green, Performance analysis of multiobjective evolutionary methods in noisy environnments, pp.29-39, 2004.

C. J. Burges and &. B. Schölkopf, Improving the Accuracy and Speed of Support Vector Machines, NIPS, vol.13, pp.500-506, 1997.

]. J. Cai-99, &. Q. Cai, and . Liu, Integration of structural and statistical Information for Unconstrained Handwritten Numeral Recognition, IEEE Trans. on PAMI, vol.21, issue.3, pp.263-270, 1999.

&. R. Califf and . Mooney, Bottom-up relational learning of pattern matching rules for information extraction, JMLR, vol.4, pp.177-210, 2003.

&. M. Ahmadi and . Shridhar, A Hierarchical Neural- Network Architecture for Handwritten Numeral Recognition, Pattern Recognition, vol.30, issue.2, pp.289-294, 1997.

]. R. Casey and &. E. Lecolinet, A survey of methods and strategies in character segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.7, pp.690-706, 1996.
DOI : 10.1109/34.506792

P. R. Cavalin, A. Britto, F. Bortolozzi, R. Sabourin, and &. L. Oliveira, An implicit segmentation-based method for recognition of handwritten strings of characters, Proceedings of the 2006 ACM symposium on Applied computing , SAC '06, pp.836-840, 2006.
DOI : 10.1145/1141277.1141468

C. C. Chang, &. J. Lin, O. Chapelle, V. Vapnik, O. Bousquet et al., LIBSVM, Machine Learning, pp.131-159, 2001.
DOI : 10.1145/1961189.1961199

&. V. Cortes and . Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

J. Cowie and &. W. Lehnert, Information extraction, Communications of the ACM, vol.39, issue.1, pp.80-91, 1996.
DOI : 10.1145/234173.234209

C. H. Coxiii, P. Coueignoux, B. B. Blesser, and &. M. Eden, Skeletons: A link between theoretical and physical letter descriptions, Pattern Recognition, vol.15, issue.1, pp.11-22, 1982.
DOI : 10.1016/0031-3203(82)90056-5

C. Cracknell, A. C. Downton, and &. L. Du, An object-oriented descriptive language to facilitate advanced handwritten form processing, Image and Vision Computing, vol.16, issue.12-13, pp.12-13, 1998.
DOI : 10.1016/S0262-8856(98)00053-5

J. P. Crettez and &. G. Lorette, Reconnaissance de l'´criture manuscrite, Techniques de l'Ingénieur, traité Informatique, 1998.

W. P. De-waard, Neural techniques and postal code detection, Pattern Recognition Letters, vol.15, issue.2, pp.199-205, 1994.
DOI : 10.1016/0167-8655(94)90049-3

]. K. Deb, S. Agrawal, A. Pratap, and &. T. Meyarivan, A fast elitist nondominated sorting genetic algorithm for multiobjective optimization : NSGA-II, Parallel problem solving from nature, pp.849-858, 2000.

P. A. Devijver and &. J. Kittler, Pattern recognition, a statistical approach, 1982.

S. Dey, Adding feedback to improve segmentation and recognition of handwritten numerals, 1999.

S. Djeziri, F. Nouboud, and &. Plamondon, Extraction of items from checks, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.749-752, 1997.
DOI : 10.1109/ICDAR.1997.620609

]. G. Dzuba-97a, A. Dzuba, D. Filatov, I. Gershuny, &. V. Kil et al., Check amount recognition based on the cross validation of courtesy and legal amount fields Automatic Bank Check Processing, World Scientific, pp.177-194, 1997.

[. E. Ashraf-03, ]. E. Ashraf, and &. A. Reda, Segmentation of connected handwritten numeral strings, Pattern Recognition, vol.36, issue.3, pp.625-634, 2003.

R. Hajj, L. Likforman-sulem, &. C. Mokbel, M. Gilloux, R. Sabourin et al., Arabic handwriting recognition using baseline dependant features and hidden Markov modeling An HMM Based Approach for Off-line Unconstrained Handwritten Word Modeling and Recognition A Statistical Approach for Phrase Location and Recognition within a Text Line : An Application to Street Name Recognition Multi-class ROC analysis from a multi-objective optimisation perspective. Pattern Recognition Letters, page in press A multiple feature/resolution approach to handprinted digit and character recognition Learning Decision Trees Using the Area Under the ROC Curve ROC optimisation of safety related systems [Filatov 95] A. Filatov, A. Gitis & I. Kil. Graph-based handwritten digit string recognition Genetic algorithm for multiobjective optimization : formulation, discussion and generalization, Charniak 93] E.Charniak. Statistical Language Learning Proceedings of the 19th International Conference on Machine Learning Proceedings of ROCAI 2004Foggia 99] P. Foggia, C. Sansone, F. Tortorella & M. Vento. Combining statistical and structural approaches for handwritten character description. IVC Proceedings of ICGA 1993 Proc. IEEEFrancesconi 01] E. Francesconi, M.Gori, S. Marinai & G.Soda. A serial combination of connectionist-based classifiers for OCR. IJDAR, pp.893-897, 1973.

&. A. Freitag, H. Mccallum, Y. Fujisawa, &. K. Nakano, and . Kurino, Information extraction with HMMs and shrinkage AAAI-99 Workshop on Machine Learning for Information Extraction [Friedrichs 05] F. Friedrichs & C. Igel. Evolutionnary tuning of multiple SVM parameters Segmentation Methods for Character Recognition : From Segmentation to Document Structure Analysis, Proc. of the IEEE, pp.31-36, 1992.

]. P. Gader and &. M. Khabou, Leave-One-Out Procedures for Nonparametric Error Estimates Automatic Feature Generation for Handwritten Digit Recognition Handwritten Word Recognition with Character and Inter-Character Neural Networks, Roli & G. Fumera. Selection of Classifiers Based on Multiple Classifier Behaviour. SSPR/SPR, pp.423-425, 1989.

M. Gilloux, B. Lemari, &. M. Leroux, N. Giusti, F. Masulli et al., A hybrid radial basis function network/hidden markov model handwritten word recognition system. ICDAR Theoretical and experimental analysis of a two-stage system for classification Language identification in the limit, Gold 03] C. Gold & P. Sollich. Model selection for support vector machine classification. Neurocomputing, pp.394-397, 1967.

E. David, M. Goldberg, &. F. Gori, and . Scarselli, Genetic algorithms in search, optimization and machine learning Are multilayer perceptrons adequate for pattern recognition and verification ?, IEEE Trans. on PAMI, vol.20, pp.1121-1132, 1989.

&. A. Govindan, Y. Shivaprasad, A. Guermeur, &. H. Eliseeff, I. Paugammoisy et al., Character recognition-a review A new multiclass svm based on a uniform convergence result [Guigue 05] V. Guigue. MéthodesMéthodes`Méthodesà noyaux pour la représentation et la discrimination de signaux non-stationnaires An introduction to variable and feature selection Off-line Handwritten Numeral String Recognition by Combining Segmentation-based and Segmentation-free Methods Handwritten Chinese character recognition by metasynthetic approach, Guyon 03] I. Guyon & A. ElisseeffHeutte 94] L. Heutte. Reconnaissance de caractères manuscrits : Applicationàplication`plicationà la lecture des chèques et des enveloppes postales, pp.1092-1096, 1990.

L. Heutte, P. Barbosa-perreira, O. Bougeois, J. V. Moreau, B. Plessis et al., Multi-bank check recognition system : consideration on the numeral amount recognition module A structural/statistical feature based vector for handwritten character recognition Decision Combination in Multiple Classifier Systems, Thèse de doctorat Proceedings of IEEE-WCCC, pp.595-618, 1994.

]. C. Hsu-02a, &. C. Hsu, and . Lin, A comparison of methods for multi-class support vector machines, IEEE Trans. on Neural Networks, vol.13, pp.415-425, 2002.

]. C. Hsu-02b, &. J. Hsu, and . Lin, A simple decomposition method for support vector machine, Machine Learning, pp.219-314, 2002.

M. K. Hu, Visual pattern recognition by moment invariants

]. J. Hu-98, &. H. Hu, and . Yan, Structural primitive extraction and coding for handwritten numeral recognition, Pattern Recognition, vol.31, issue.5, pp.493-509, 1998.
DOI : 10.1016/S0031-3203(97)00095-2

&. C. Huang and . Suen, A method of combining multiple experts for the recognition of unconstrained handwritten numerals, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.1, pp.90-94, 1995.
DOI : 10.1109/34.368145

&. Huang and . Wang, A GA-based feature selection and parameters optimization for support vector machine. Expert systems with application, pp.231-240, 2006.

&. S. Hwang and . Bang, Recognition of unconstrained handwritten numerals by a radial basis function neural network classifier, Pattern Recognition Letters, vol.18, issue.7, pp.657-664, 1997.
DOI : 10.1016/S0167-8655(97)00056-1

]. S. Impedovo-97a, G. Impedovo, &. A. Pirlo, and . Salzo, A new engineered system Automatic Bank Check Processing, World Scientific, pp.5-42, 1997.

]. S. Impedovo-97b, P. S. Impedovo, &. H. Wang, and . Bunke, Automatic bankcheck processing, volume 28 of Series in Machine Perception and Artificial Intelligence, World Scientific, 1997.

]. R. Jacobs, M. I. Jordan, S. J. Nowlan, and &. G. Hinton, Adaptive Mixtures of Local Experts, Neural Computation, vol.4, issue.1, pp.79-87, 1991.
DOI : 10.1162/neco.1989.1.2.281

K. Anil, &. Jain, K. Sushil, and . Bhattacharjee, Address block location on envelopes using Gabor filters, Pattern Recognition, vol.25, issue.12, pp.1459-1477, 1992.

]. A. Jain, R. P. Duin, and &. J. Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.4-37, 2000.
DOI : 10.1109/34.824819

]. K. Jung and &. J. Kim, On-line recognition of cursive Korean characters using graph representation, Pattern Recognition, vol.33, issue.3, pp.399-412, 2000.
DOI : 10.1016/S0031-3203(99)00062-X

M. N. Kapp, C. Freitas, and &. R. Sabourin, Handwritten Brazilian Month Recognition: An Analysis of Two NN Architectures and a Rejection Mechanism, Ninth International Workshop on Frontiers in Handwriting Recognition, pp.209-214, 2004.
DOI : 10.1109/IWFHR.2004.53

]. K. Keeni, H. Shimodaira, T. Nishino, and &. Y. Tan, Recognition of Devanagari Characters Using Neural Networks, IEICE, issue.5, pp.523-528, 1996.

S. S. Keerthi, Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms, IEEE Transactions on Neural Networks, vol.13, issue.5, pp.1225-1229, 2002.
DOI : 10.1109/TNN.2002.1031955

V. Khare, X. Yao, and &. K. Deb, Performance scaling of multiobjective evolutionary algorithm, pp.1-70, 2002.

]. G. Kim-97a, &. V. Kim, and . Govindaraju, Banckcheck recognition using cross validation between legal and courtesy amount, Automatic Bank Check Processing, pp.195-212, 1997.

]. G. Kim-97b, &. V. Kim, and . Govindaraju, A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications

]. G. Kim and &. V. Govindaraju, Handwritten phrase recognition as applied to street name images, Pattern Recognition, vol.31, issue.1, pp.41-51, 1998.
DOI : 10.1016/S0031-3203(97)00023-X

]. G. Kim, V. Govindaraju, and &. S. Srihari, An architecture for handwritten text recognition systems, International Journal on Document Analysis and Recognition, vol.2, issue.1, pp.37-44, 1999.
DOI : 10.1007/s100320050035

K. Ho-yon, K. Kim, &. Lim, and . Nam, Handwritten numeral string recognition using neural network classifier trained with negative data, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, p.395, 2002.
DOI : 10.1109/IWFHR.2002.1030942

]. K. Kim-02b, J. H. Kim, &. Y. Kim, and . Suen, Segmentation-based recognition of handwritten touching pairs of digits using structural features, Pattern Recognition Letters, vol.23, issue.1, pp.13-24, 2002.

]. K. Kim, J. J. Park, Y. G. Song, I. C. Kim, and &. Y. Suen, Recognition of Handwritten Numerals Using a Combined Classifier with Hybrid Features, pp.992-1000, 2004.
DOI : 10.1007/978-3-540-27868-9_109

F. Kimura and &. M. Shridhar, Handwritten numerical recognition based on multiple algorithms, Pattern Recognition, vol.24, issue.10, pp.969-983, 1991.
DOI : 10.1016/0031-3203(91)90094-L

F. Kimura, S. Tsuruoka, Y. Miyake, and &. M. Shridhar, Improvements of a lexicon directed algorithm for recognition of unconstrained handwritten words, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93), pp.785-793, 1994.
DOI : 10.1109/ICDAR.1993.395791

J. Kittler, M. Hatef, R. P. Duin, and &. J. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998.
DOI : 10.1109/34.667881

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.3365

S. Knerr, V. Asimov, O. Baret, N. Gorsky, &. J. Simon et al., The a2ia intercheque system : Courtesy amount and legal amount recognition for french checks, pp.43-86

S. Knerr and &. E. Augustin, A neural network-hidden Markov model hybrid for cursive word recognition, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1518-1520, 1998.
DOI : 10.1109/ICPR.1998.711996

G. Koch, Catégorisation automatique de documents manuscrits : application aux courriers entrants, Thèse de doctorat, 2006.

A. L. Koerich, Y. Leydier, R. Sabourin, and &. Y. Suen, A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, pp.99-105, 2002.
DOI : 10.1109/IWFHR.2002.1030893

]. A. Koerich-03a and . Koerich, Unconstrained Handwritten Character Recognition Using Different Classification Strategies, 2003.

]. A. Koerich-03b, R. Koerich, &. Y. Sabourin, and . Suen, Large vocabulary off-line handwriting recognition: A survey, Pattern Analysis & Applications, vol.6, issue.2, pp.97-121, 2003.
DOI : 10.1007/s10044-002-0169-3

M. Koga, R. Mine, H. Sako, and &. H. Fujisawa, A recognition method of machine-printed monetary amounts based on the two-dimensional segmentation and the bottom-up parsing. ICDAR, pp.968-971, 2001.

R. Kohavi and &. G. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

T. Kristjannson, A. Culotta, P. Viola, and &. A. Mccallum, Interactive information extraction with constrained conditional random fields, pp.412-418, 2004.

M. Kupinski and &. M. Anastasio, Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characterisitic curves, IEEE Trans. Med. Imaging, vol.8, pp.675-685, 1999.

]. J. Lafferty, A. Mccallum, and &. F. Pereira, Conditional Random Fields : Probabilistic Models for Segmenting and Labeling Sequence Data, Proc. 18th International Conf. on Machine Learning, pp.282-289, 2001.

T. Landgrebe, P. Paclik, and &. D. Tax, Optimising Two-Stage Recognition Systems, MCS' 05, pp.206-215, 2005.
DOI : 10.1007/11494683_21

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.1479

S. M. Lavalle and &. M. Branicky, On the Relationship between Classical Grid Search and Probabilistic Roadmaps, The International Journal of Robotics Research, vol.23, issue.7-8, pp.673-692, 2002.
DOI : 10.1177/0278364904045481

V. Di-lecce, G. Dimauro, A. Guerriero, &. A. Salzo, S. Impedovo et al., Classifier Combination : the role of a priori knowledge, IWFHR VII, pp.143-152, 2000.

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.
DOI : 10.1007/BF00133697

Y. Lecun, L. D. Jackel, L. Bottou, A. Brunot, C. Cortes et al., Comparison of learning algorithms for handwritten digit recognition, pp.53-60, 1995.

L. Lecun, Y. Bottou, &. P. Bengio, S. W. Haffner, and . Lee, Gradient- Based Learning Applied to Document Recognition Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural Network, Proceedings of the IEEE, pp.2278-2324, 1996.

T. R. Leek, Information extraction using hidden Markov models, 1997.

C. S. Lei, X. Q. Liu, &. Q. Ding, and . Fu, A Recognition Based System for Segmentation of Touching Handwritten Numeral Strings, pp.294-299, 2004.

M. Leroux, E. Lethelier, M. Gilloux, and &. B. Lemarie, Automatic reading of handwritten amounts on french checks Automatic bank check processing, World Scientific, pp.157-176, 1997.

E. Lethelier, M. Leroux, and &. M. Gilloux, An automatic reading system for handwritten numeral amounts on French checks, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.92-97, 1995.
DOI : 10.1109/ICDAR.1995.598951

E. Lethelier, Combinaison des concepts de segmentation et de reconnaissance pour l'´ ecriture manuscrite hors ligne : application au traitement des montants numériques de chèques, Thèse de doctorat, 1996.

]. J. Lii-93, P. Lii, &. S. Palumbo, and . Srihari, Address block location using character recognition and address syntax, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93), pp.330-335, 1993.
DOI : 10.1109/ICDAR.1993.395721

. Likforman-sulem-95a-]-l, &. C. Likforman-sulem, and . Faure, Une methode de resolution des conflits d'alignements pour la segmentation des documents manuscrits, Traitement du Signal, vol.12, pp.541-549, 1995.

. Likforman-sulem-95b-]-l, A. Likforman-sulem, &. C. Hanimyan, and . Faure, A Hough based algorithm for extracting text lines in handwritten documents, Proceedings of 3rd International Conference on Document Analysis and Recognition, pp.774-777, 1995.
DOI : 10.1109/ICDAR.1995.602017

]. C. Liu-02a, K. Liu, H. Nakashima, &. H. Sako, and . Fujisawa, Handwritten Digit Recognition Using State-of-the-Art Techniques, pp.320-325, 2002.

]. C. Liu-02b, H. Liu, &. H. Sako, and . Fujisawa, Performance evaluation of pattern classifiers for handwritten character recognition, IJDAR, vol.4, pp.191-204, 2002.

]. J. Liu-02c, &. P. Liu, and . Gader, Neural Networks with Enhanced Outlier Rejection Ability for Off-Line Handwritten Word Recognition Pattern Recognition, Pattern Recognition, vol.35, pp.2061-2071, 2002.

C. L. Liu, H. Sako, and &. H. Fujisawa, Effects of Classifier Structures and Training Regimes on Integrated Segmentation and Recognition of Handwritten Numeral Strings, IEEE Trans. on PAMI, vol.26, pp.1395-1407, 2004.

C. L. Liu and &. H. Sako, Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition, Pattern Recognition, vol.39, issue.4, pp.669-681, 2006.
DOI : 10.1016/j.patcog.2005.04.021

D. Lopresti, String techniques for detecting duplicates in document databases, International Journal on Document Analysis and Recognition, vol.2, issue.4, pp.186-199, 2000.
DOI : 10.1007/s100320050005

]. G. Lorette and &. Y. Lecourtier, Reconnaissance et interprétation de textes manuscrits hors-ligne : Unprobì eme d'analyse de scènes ? CNED'92, pp.109-135, 1992.

]. G. Lorette, Handwriting Recognition or reading ? What is the situation at the dawn of the 3rd Millenium ? IJDAR, pp.2-12, 1999.

Z. Lu, Z. Chi, W. C. Siu, and &. P. Shi, A background-thinning-based approach for separating and recognizing connected handwritten digit strings, Pattern Recognition, vol.32, issue.6, pp.921-933, 1999.
DOI : 10.1016/S0031-3203(98)00123-X

]. V. Madasu, M. H. Yusof, M. Hanmandlu, and &. K. Kubik, Automatic extraction of signatures from bank cheques and other documents, Biennial Australian Pattern Recognition Conference, vol.2, pp.591-600, 2003.

S. Madhvanath and &. V. Govindaraju, Holistic lexicon reduction, IWFHR, pp.71-81, 1993.

&. S. Lee and . Srihari, Reading handwritten US census forms, ICDAR, vol.1, pp.82-87, 1995.

S. Manke, M. Finke, and &. A. Waibel, A fast search technique for large vocabulary on-line handwriting recognition, IWFHR, pp.437-444, 1996.

]. U. Marti and &. H. Bunke, A full English sentence database for off-line handwriting recognition, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), pp.705-708, 1999.
DOI : 10.1109/ICDAR.1999.791885

]. U. Marti-01a, &. H. Marti, and . Bunke, Text line segmentation and word recognition in a system for general writer independent handwriting recognition, Proceedings of Sixth International Conference on Document Analysis and Recognition, p.159, 2001.
DOI : 10.1109/ICDAR.2001.953775

]. U. Marti-01b, &. H. Marti, and . Bunke, Using a statistical Language model to improve the performance of an HMM-based cursive handwriting recognition system, IJPRAI, vol.15, pp.65-90, 2001.

]. C. Martin-93 and . Martin, Centered-Object Integrated Segmentation and Recognition of Overlapping Handprinted Characters, Neural Computation, vol.5, issue.3, pp.419-429, 1993.
DOI : 10.1016/0262-8856(87)90071-0

. Milewski and . Milewski, Automatic Recognition Of Handwritten Medical For Search Engines. Rapport technique, 2006.

]. R. Milewski-06b, &. V. Milewski, and . Govindaraju, Extraction of Handwritten Text from Carbon Copy Medical Form Images, pp.106-116, 2006.
DOI : 10.1007/11669487_10

J. Milgram, R. Sabourin, and &. M. Cheriet, An hybrid classification system which combines model-based and discriminative approaches, ICPR, vol.1, pp.155-162, 2004.

M. Cheriet and &. R. Sabourin, Estimating accurate multi-class probabilities with support vector machines, IJCNN, vol.3, pp.1906-1911, 2005.

D. Miller, S. Boisen, R. Schwartz, R. Stone, and &. R. Weischedel, Named entity extraction from noisy input, Proceedings of the sixth conference on Applied natural language processing -, pp.316-324, 2000.
DOI : 10.3115/974147.974191

M. Mohammed and &. P. Gader, Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.5, pp.548-554, 1996.
DOI : 10.1109/34.494644

]. N. Morgan, H. Bourlard, S. Renls, M. Cohen, and &. H. Franco, Hybrid neural network/hidden Markov model systems for continuous speech recognition, IJPRAI, vol.7, issue.4, 1993.

M. Morita, R. Sabourin, F. Bortolozzi, and &. C. Suen, Segmentation and recognition of handwritten dates, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, 2006.
DOI : 10.1109/IWFHR.2002.1030894

H. Mouchère, E. Anquetil, and &. N. Ragot, Etude et gestion des types de rejet pour l'optimisation de classifieurs, 2006.

R. Mozer, M. D. Dodier, C. Colagrosso, &. R. Guerra-salcedo, and . Wolniewicz, Prodding the ROC Curve : Constrained Optimization of Classifier Performance, NIPS, pp.1409-1415, 2002.

P. Mulbregt, I. Van, L. Gillick, S. Lowe, &. J. Yamron et al., Optimizing F-Measure with Support Vector Machines. FLAIRS Conference Twenty years of document image analysis in PAMI Analysis of Printed Forms. Handbook of Character Recognition and Document Image Analysis [Nosary 02] A. Nosary. Reconnaissance automatique de textes Manuscrits par adaptation du scripteur Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition [Oh 02] I. Oh & C.Y. Suen. A class-modular feedforward neural network for handwriting recognition Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition Automatic Recognition of Handwritten Numeral Strings : A Recognition and Verification Strategy Impacts of Verification on a numeral string recognition system, Text Segmentation and Topic Tracking on Broadcast News Via a Hidden Markov Model Approach. ICSLP'98 Thèse de doctoratOliveira 00] L.E. Oliveira, E. Lethelier & F. Bortolozzi. A New Segmentation Approach for Handwritten Digits. ICPROliveira 04] L.S. Oliveira & R. Sabourin. Support Vector Machines for Handwritten Numerical String RecognitionOliveira 06] L.S. Oliveira, M. Morita & R. Sabourin. Feature Selection for Ensembles Applied to Handwriting Recognition. IJDAR, pp.2519-2522, 1997.

S. Omachi, F. Sun, and &. H. Aso, A noise-adaptive discriminant function and its application to blurred machine-printed Kanji recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.3, pp.314-319, 2000.
DOI : 10.1109/34.841761

B. Oommen and &. R. Loke, Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions, Pattern Recognition, vol.30, issue.5, pp.789-800, 1997.
DOI : 10.1016/S0031-3203(96)00101-X

E. Osuna, R. Freund, and &. F. Girosi, Support vector machines : Training and applications, 1997.

U. Pal, A. Bela¨?dbela¨?d, and &. C. Choisy, Water Reservoir Based Approach for Touching Numeral Segmentation. ICDAR, Pal 03] U. Pal, A. Bela¨?dBela¨?d & C. Choisy. Touching Numeral Segmentation Using Water Reservoir Concept, pp.892-897, 2001.
URL : https://hal.archives-ouvertes.fr/inria-00100455

R. Palacios and &. A. Gupta, A system for processing handwritten bank checks automatically. rapport interne, 1997.

H. S. Park and &. S. Lee, Off-line recognition of large-set handwritten characters with multiple hidden Markov models, Pattern Recognition, vol.29, issue.2, pp.231-244, 1996.
DOI : 10.1016/0031-3203(95)00081-X

M. Pfister and S. Rojas, Recognition of Handwritten ZIP Codes in a Real-World Non-Standard- Letter Sorting System, Applied Intelligence, vol.12, issue.1/2, pp.95-114, 2000.
DOI : 10.1023/A:1008316121543

V. Pillet, Méthodologie d'extraction automatique d'informationàtion`tionà partir de la littérature scientifique en vue d'alimenter un nouveau système d'information, 2000.

D. Pinto, A. Mccallum, X. Wei, and &. W. Croft, Table extraction using conditional random fields, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval , SIGIR '03, pp.235-242, 2003.
DOI : 10.1145/860435.860479

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.320

J. F. Pitrelli and &. M. Perrone, Confidence-scoring post-processing for off-line handwritten-character recognition verification, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp.278-282, 2003.
DOI : 10.1109/ICDAR.2003.1227673

R. Plamondon and &. S. Srihari, Online and off-line handwriting recognition: a comprehensive survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.63-84, 2000.
DOI : 10.1109/34.824821

]. J. Platt-99a and . Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization Advances in Kernel Methods -Support Vector Learning, pp.185-208, 1999.

]. J. Platt-99b and . Platt, Probabilities for SV Machines Advances in Large Margin Classifiers, pp.61-74, 1999.

T. Poibeau, Extraction d'informationàinformation`informationà base de connaissances hybrides, 2002.

E. Poisson, Architecture et apprentissage d'un système hybride neuro-markovien pour la reconnaissance de l'´ ecriture manuscrite en-ligne, 2005.

R. K. Powalka, N. Sherkat, &. Robert, and J. Whitrow, The Use Of Word Shape Information For Cursive Script Recognition, IWFHR, pp.67-76, 1994.

R. K. Powalka, N. Sherkat, and &. R. Withrow, Word shape analysis for a hybrid recognition system, Pattern Recognition, vol.30, issue.3, pp.421-445, 1997.
DOI : 10.1016/S0031-3203(96)00093-3

L. Prevost and &. M. Milgram, Coopération pour la Reconnaissance de Caractères Dynamique Isolés, RFIA, vol.3, pp.233-240, 1998.

L. Prevost, C. Michel-sendis, A. Moises, L. Oudot, and &. M. Milgram, Combining model-based and discriminative classifiers: application to handwritten character recognition, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp.31-35, 2003.
DOI : 10.1109/ICDAR.2003.1227623

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9463

S. Procter and &. J. Elms, The recognition of handwritten digit strings of unknown length using hidden Markov models, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1515-1517, 1998.
DOI : 10.1109/ICPR.1998.711995

J. Antonio-pérez-ortiz, &. Mikel, and L. Forcada, Part-ofspeech tagging with recurrent neural networks, IJCNN, pp.1588-1592, 2001.

]. J. Quinlan-93 and . Quinlan, C4.5 : programs for machine learning, 1993.

A. F. Rahman, &. C. Fairhurst, A. F. Rahman, &. C. Fairhurst, S. Ramdane et al., A New Hybrid approach in combining multiple experts to recognize handwritten numeral Multiple classifier decision combination strategies for character recognition : A review Classification of forms with handwritten fields by planar hidden Markov models Two Decades Of Statistical Language Modeling : Where Do We Go From Here ? Machine recognition of handwritten words : A project report Feature Extraction of Handwritten Japanese Characters by Spline Functions for Relaxation Matching, Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition Readings in Speech Recognition Rakotomamonjy. Optimizing AUC with Support Vector Machine. European Conference on Artificial Intelligence Workshop on ROC Curve and AI Hybrid System for Recognition of Handwritten Symbols on the Base of Structural Methods and Neural Networks. VI02 Multiobjective learning via genetic algorithms. IJCAISeni 96] G. Seni, R.K. Srihari & N. Nasrabadi. Large Vocabulary Recognition Of Online Handwritten Cursive Words, pp.267-296, 1973.

A. W. Senior and &. A. Robinson, An off-line cursive handwriting recognition system, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.309-321, 1998.
DOI : 10.1109/34.667887

]. F. Sha-03, &. F. Sha, and . Pereira, Shallow parsing with conditional random fields, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology , NAACL '03, pp.134-141, 2003.
DOI : 10.3115/1073445.1073473

Z. Shi, S. N. Srihari, Y. Shin, and &. V. Ramanaprasad, A System for Segmentation and Recognition of Totally Unconstrained Handwritten Numeral Strings. ICDAR, p.455, 1997.

M. Shridhar and &. A. Badreldin, Recognition of isolated and simply connected handwritten numerals, Proc. IEEE International Conference on Systems, Man and Cybernetics, pp.142-146, 1984.
DOI : 10.1016/0031-3203(86)90025-7

P. Slavik and &. Venu-govindaraju, Equivalence of different methods for slant and skew corrections in word recognition applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.3, pp.323-326, 2001.
DOI : 10.1109/34.910885

S. Soderland and &. W. Lehnert, Wrap-Up : a Trainable Discourse Module for Information Extraction, Journal of Artificial Intelligence Research, vol.2, pp.131-158, 1994.

K. Rohini, &. Srihari, M. Charlotte, and . Baltus, Incorporating Syntactic Constraints in Recognizing Handwritten Sentences, IJCAI, pp.1262-1267, 1993.

N. Sargur, &. Srihari, J. Edward, and . Kuebert, Integration of handwritten address interpretation technology into the United States Postal Service Remote Computer Reader system, IC- DAR, pp.892-896, 1997.

]. S. Srihari-97b, &. E. Srihari, and . Keubert, Integration of handwritten address interpretation technology into the united states postal service remote computer reader system, pp.892-896, 1997.

N. Srinivas and &. K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionnary Computation, pp.221-248, 1994.

C. C. Suen, &. T. Tappert, and . Wakahara, The State of the Art in On-Line Handwriting Recognition, IEEE Trans. on PAMI, vol.12, issue.8, pp.787-808, 1990.

M. Suwa and &. S. Naoi, Segmentation of Handwritten Numerals by Graph Representation. IWFHR, pp.334-339, 2004.
DOI : 10.1109/iwfhr.2004.91

]. K. Taghva, J. S. Coombs, R. Pereda, and &. T. Nartker, Address extraction using hidden Markov models. Document Recognition and Retrieval XII, Proceedings of the SPIE, pp.119-126, 2004.

]. K. Takahashi and &. D. Nishiwaki, A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp.268-272, 2003.
DOI : 10.1109/ICDAR.2003.1227671

C. C. Tappert, Adaptive on-line handwriting recognition. ICPR, Duin. Combining One-Class Classifiers. MCS '01, pp.1004-1007, 1984.

T. Taxt, J. V. Olafsdottir, and &. M. Doehlen, Recognition of handwritten symbols, Pattern Recognition, vol.23, issue.11, pp.1155-1166, 1990.
DOI : 10.1016/0031-3203(90)90113-Y

O. Trier and &. A. Jain, Goal-directed evaluation of binarization methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.12, pp.1191-1201, 1995.
DOI : 10.1109/34.476511

O. D. Trier, A. K. Jain, and &. T. Taxt, Feature extraction methods for character recognition-A survey, Pattern Recognition, vol.29, issue.4, pp.641-662, 1996.
DOI : 10.1016/0031-3203(95)00118-2

]. C. Tsang-98, &. L. Tsang, and . Chung, Development of a structural deformable model for handwriting recognition, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1130-1133, 1998.
DOI : 10.1109/ICPR.1998.711894

S. Tulyakov and &. V. Govindaraju, Postal Address Block Location by Contour Clustering. ICDAR, pp.429-432, 2003.

A. Vinciarelli, S. Bengio, and &. H. Bunke, Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models, IEEE Trans. on PAMI, vol.26, issue.6, pp.709-720, 2004.

L. Vuurpijl, L. Schomaker, and &. M. Van-erp, Architectures for detecting and solving conflicts: two-stage classification and support vector classifiers, International Journal on Document Analysis and Recognition, vol.5, issue.4, pp.213-223, 2003.
DOI : 10.1007/s10032-003-0104-1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.7941

]. G. Zhang, Neural networks for classification: a survey, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.30, issue.4, pp.641-662, 2000.
DOI : 10.1109/5326.897072

&. C. Zhang and . Suen, Recognition of courtesy amounts on bank checks based on a segmentation approach. FHR02, pp.298-302, 2002.

&. S. Liu and . Xia, Support Vector Machine and its Application in Handwritten Numeral Recognition, pp.720-723, 2000.

J. Zhou, Q. Gan, A. Krzyzak, and &. C. Suen, Recognition and verification of touching handwritten numerals, pp.179-188, 2000.

X. Zhu, Y. Shi, and &. S. Wang, A New Distinguishing Algorithm of Connected Character Image Based on Fourier Transform. icdar, p.788, 1999.

E. Zitzler and &. L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, vol.3, issue.4, pp.257-271, 1999.
DOI : 10.1109/4235.797969

E. Zitzler, M. Laumanns, and &. L. Thiele, SPEA2 : Improving the strength pareto evolutionary algorithm, 2001.

H. Zouari, L. Heutte, Y. Lecourtier, and &. A. Alimi, Un panorama des méthodes de combinaison de classifieurs en reconnaissance de formes, RFIA, vol.2, pp.499-508, 2002.