M. Abadi, Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016.

O. Abdel-hamid, Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4277-4280, 2012.
DOI : 10.1109/ICASSP.2012.6288864

URL : http://www.cs.toronto.edu/%7Easamir/papers/icassp12_cnn.pdf

A. Abdulkader, A Two-Tier Arabic Offline Handwriting Recognition Based on Conditional Joining Rules, Arabic and Chinese Handwriting Recognition, pp.70-81, 2008.
DOI : 10.1007/978-3-540-78199-8_5

M. Adda-decker, A corpus-based decompounding algorithm for German lexical modeling in LVCSR, 2003.

M. Adda-decker and G. Adda, Morphological decomposition for ASR in German, Phonetics and Phonology in ASR, pp.129-143, 2000.

I. Ahmad, A. Gernot, and . Fink, Class-Based Contextual Modeling for Handwritten Arabic Text Recognition, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.369-374, 2016.
DOI : 10.1109/ICFHR.2016.0107

. Ait-mohand, T. Kamel, N. Paquet, and . Ragot, Combining Structure and Parameter Adaptation of HMMs for Printed Text Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.9, pp.1716-1732, 2014.
DOI : 10.1109/TPAMI.2014.2306423

URL : https://hal.archives-ouvertes.fr/hal-01084973

C. Allauzen, OpenFst: A General and Efficient Weighted Finite-State Transducer Library, International Conference on Implementation and Application of Automata, pp.11-23, 2007.
DOI : 10.1007/978-3-540-76336-9_3

N. Arica, T. Fatos, and . Yarman, An overview of character recognition focused on off-line handwriting, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.31, issue.2, pp.31-216, 2001.
DOI : 10.1109/5326.941845

M. Bacchiani, Design of a speech recognition system based on acoustically derived segmental units ICASSP-96, Acoustics, Speech, and Signal Processing IEEE International Conference on, pp.443-446, 1996.

S. Bahrampour, Comparative study of caffe, neon, theano, and torch for deep learning, 2015.

S. Bartlett, G. Kondrak, and C. Cherry, On the syllabification of phonemes, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics on, NAACL '09, pp.308-316, 2009.
DOI : 10.3115/1620754.1620799

Y. Bassil and M. Alwani, Ocr post-processing error correction algorithm using google online spelling suggestion, 2012.
DOI : 10.14569/ijacsa.2012.030217

URL : http://thesai.org/Downloads/Volume3No2/Paper%2017%20-%20Post-Editing%20Error%20Correction%20Algorithm%20for%20Speech%20Recognition%20using%20Bing%20Spelling%20Suggestion.pdf

F. Bastien, Theano: new features and speed improvements, 2012.

L. Bauer, Manual of information to accompany the Wellington corpus of written New Zealand English, 1993.

B. Beeton, Hyphenation exception log, p.160, 2010.

Y. Bengio, P. Simard, and P. Frasconi, Learning longterm dependencies with gradient descent is difficult, IEEE transactions on neural networks 5.2, pp.157-166, 1994.
DOI : 10.1109/72.279181

URL : http://www.research.microsoft.com/~patrice/PDF/long_term.pdf

Y. Bengio, LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition, Neural Computation 7.6, pp.1289-1303, 1995.
DOI : 10.1109/34.57669

URL : http://www.iro.umontreal.ca/~lisa/pointeurs/lerec-nc95.pdf

Y. Bengio, Neural Probabilistic Language Models, Journal of machine learning research 3.Feb, pp.1137-1155, 2003.
DOI : 10.1007/3-540-33486-6_6

URL : https://hal.archives-ouvertes.fr/hal-01434258

Y. Bengio, Curriculum learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.41-48, 2009.
DOI : 10.1145/1553374.1553380

M. Benzeghiba, J. Faouzi, C. Louradour, and . Kermorvant, Hybrid word/Part-of-Arabic-Word Language Models for arabic text document recognition, 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp.671-675, 2015.
DOI : 10.1109/ICDAR.2015.7333846

A. L. Berger, V. J. , D. Pietra, and S. Pietra, A maximum entropy approach to natural language processing, Computational linguistics 22.1, pp.39-71, 1996.

F. Bimbot, Modeles de séquencesa horizon variable: Multigrams, Proc. XX-emes Journées d'Etude sur la Parole, pp.467-472, 1994.

M. Bisani and H. Ney, Open vocabulary speech recognition with flat hybrid models, pp.725-728, 2005.

W. Bledsoe, I. Wilson, and . Browning, Pattern recognition and reading by machine, Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference on, IRE-AIEE-ACM '59 (Eastern), 1959.
DOI : 10.1145/1460299.1460326

T. Bluche, Deep Neural Networks for Large Vocabulary Handwritten Text Recognition, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01249405

T. Bluche, H. Ney, and C. Kermorvant, Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition, 2013 12th International Conference on Document Analysis and Recognition, pp.285-289, 2013.
DOI : 10.1109/ICDAR.2013.64

T. Bluche, H. Ney, and C. Kermorvant, Tandem HMM with convolutional neural network for handwritten word recognition, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2390-2394, 2013.
DOI : 10.1109/ICASSP.2013.6638083

T. Bluche, H. Ney, and C. Kermorvant, A Comparison of Sequence-Trained Deep Neural Networks and Recurrent Neural Networks Optical Modeling for Handwriting Recognition, International Conference on Statistical Language and Speech Processing, pp.199-210, 2014.
DOI : 10.1007/978-3-319-11397-5_15

T. Bluche, The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation, 2014 11th IAPR International Workshop on Document Analysis Systems, pp.161-165, 2014.
DOI : 10.1109/DAS.2014.40

H. Bourlard, J. Christian, and . Wellekens, Links between Markov models and multilayer perceptrons, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.12, pp.1167-1178, 1990.
DOI : 10.1109/34.62605

A. Brakensiek, J. Rottland, and G. Rigoll, Handwritten address recognition with open vocabulary using character n-grams, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, pp.357-362, 2002.
DOI : 10.1109/IWFHR.2002.1030936

A. Z. Broder, Syntactic clustering of the Web, Computer Networks and ISDN Systems, pp.8-13, 1997.
DOI : 10.1016/S0169-7552(97)00031-7

K. Brown, . Brown, and F. Peter, Encyclopedia of language and linguistics A statistical approach to machine translation, Computational linguistics 16, pp.79-85, 1990.

S. Bukhari, F. Saqib, . Shafait, M. Thomas, and . Breuel, Script-Independent Handwritten Textlines Segmentation Using Active Contours, 2009 10th International Conference on Document Analysis and Recognition, pp.446-450, 2009.
DOI : 10.1109/ICDAR.2009.206

J. A. Bullinaria, Introduction to neural networks ", 2004.

H. Bunke, S. Bengio, and A. Vinciarelli, Offline recognition of unconstrained handwritten texts using HMMs and statistical language models, IEEE transactions on Pattern analysis and Machine intelligence 26.6, pp.709-720, 2004.
DOI : 10.1109/TPAMI.2004.14

R. G. Casey and E. Lecolinet, A survey of methods and strategies in character segmentation, IEEE transactions on pattern analysis and machine intelligence 18.7, pp.690-706, 1996.
DOI : 10.1109/34.506792

C. Chatelain, L. Heutte, and T. Paquet, Segmentationdriven recognition applied to numerical field extraction from handwritten incoming mail documents " . In: International Workshop on Document Analysis Systems, pp.564-575, 2006.
DOI : 10.1007/11669487_50

URL : https://hal.archives-ouvertes.fr/hal-00435969

S. F. Chen and J. Goodman, An empirical study of smoothing techniques for language modeling, Proceedings of the 34th annual meeting on Association for Computational Linguistics, pp.310-318, 1996.

F. Chollet, Keras: Deep learning library for theano and tensorflow, 2015.

N. Chomsky, Syntactic structures, 2002.
DOI : 10.1515/9783110218329

P. A. Chou and T. Lookabaugh, Variable dimension vector quantization of linear predictive coefficients of speech, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, p.505, 1994.
DOI : 10.1109/ICASSP.1994.389245

. Choueiter and F. Ghinwa, Linguistically-motivated sub-word modeling with applications to speech recognition, 2009.

J. Chung, Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014.

R. Collobert, S. Bengio, and J. Mariéthoz, Torch: a modular machine learning software library, 2002.

B. Comrie, S. Matthews, and M. Polinsky, The atlas of languages: The origin and development of languages throughout the world. Blooms- bury, 1997.

M. Creutz, Morph-based speech recognition and modeling of out-of-vocabulary words across languages, ACM Transactions on Speech and Language Processing, vol.5, issue.1, p.3, 2007.
DOI : 10.1145/1322391.1322394

S. Deligne and F. Bimbot, Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams, 1995 International Conference on Acoustics, Speech, and Signal Processing, pp.169-172, 1995.
DOI : 10.1109/ICASSP.1995.479391

S. Deligne and F. Bimbot, Inference of variable-length linguistic and acoustic units by multigrams, Speech Communication, vol.23, issue.3, pp.223-241, 1997.
DOI : 10.1016/S0167-6393(97)00048-4

S. Deligne and Y. Sagisaka, Statistical language modeling with a class-basedn-multigram model, Computer Speech & Language, vol.14, issue.3, pp.261-279, 2000.
DOI : 10.1006/csla.2000.0146

S. Deligne, F. Yvon, and F. Bimbot, Introducing statistical dependencies and structural constraints in variable-length sequence models, International Colloquium on Grammatical Inference, pp.156-167, 1996.
DOI : 10.1007/BFb0033351

URL : http://www-inf.enst.fr/~research/publications_ec/yvon/yvon_96d.ps

D. Pietra and S. , Adaptive language modeling using minimum discriminant estimation, Proceedings of the workshop on Speech and Natural Language, pp.103-106, 1992.

D. Diringer, The alphabet: a key to the history of mankind Fast and robust training of recurrent neural networks for offline handwriting recognition, Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on. IEEE, pp.279-284, 1951.

P. Doetsch, Returnn: The RWTH extensible training framework for universal recurrent neural networks, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
DOI : 10.1109/ICASSP.2017.7953177

URL : http://arxiv.org/pdf/1608.00895

P. Dreuw, RWTH OCR: A large vocabulary optical character recognition system for Arabic scripts " . In: Guide to OCR for Arabic Scripts, pp.215-254, 2012.
DOI : 10.1007/978-1-4471-4072-6_9

D. Eck and J. Schmidhuber, A first look at music composition using lstm recurrent neural networks, Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale 103, 2002.

A. El-desoky, Investigating the use of morphological decomposition and diacritization for improving Arabic LVCSR, In: INTERSPEECH, pp.2679-2682, 2009.

. El-sheikh, S. Talaat, M. Ramez, and . Guindi, Computer recognition of arabic cursive scripts, Pattern Recognition, vol.21, issue.4, pp.293-302, 1988.
DOI : 10.1016/0031-3203(88)90042-8

. El-yacoubi, A. Mounim, M. Gilloux, and J. Bertille, A statistical approach for phrase location and recognition within a text line: an application to street name recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.2, pp.172-188, 2002.
DOI : 10.1109/34.982898

C. Farabet, Learning Hierarchical Features for Scene Labeling, IEEE transactions on pattern analysis and machine intelligence 35, pp.1915-1929, 2013.
DOI : 10.1109/TPAMI.2012.231

URL : https://hal.archives-ouvertes.fr/hal-00742077

J. L. Feild, G. Erik, . Learned-miller, A. David, and . Smith, Using a Probabilistic Syllable Model to Improve Scene Text Recognition, 2013 12th International Conference on Document Analysis and Recognition, pp.897-901, 2013.
DOI : 10.1109/ICDAR.2013.183

URL : http://people.cs.umass.edu/~elm/papers/syllable_icdar13.pdf

G. A. Fink, Markov models for pattern recognition: from theory to applications, 2014.
DOI : 10.1007/978-1-4471-6308-4

J. Fiscus, Sclite scoring package, version 1.5. US National Institute of Standard Technology (NIST), 2015.

J. Fiscus, Sclite scoring package version 1.5, US National Institute of Standard Technology (NIST), URL http, 1998.

D. Flipo, B. Gaulle, and K. Vancauwenberghe, Motifs françis de césure typographique, Cahiers gutenberg n, 1994.
DOI : 10.5802/cg.150

W. Francis, H. Nelson, and . Ku?era, Manual of information to accompany a standard corpus of present-day edited American English, for use with digital computers, 1979.

Y. Freund, E. Robert, and . Schapire, Large margin classification using the perceptron algorithm, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.277-296, 1999.
DOI : 10.1145/279943.279985

URL : http://www.research.att.com/~schapire/cgi-bin/uncompress-papers/FreundSc98.ps

A. Ganapathiraju, Syllable-based large vocabulary continuous speech recognition, IEEE Transactions on speech and audio processing 9.4, pp.358-366, 2001.
DOI : 10.1109/89.917681

B. Gatos, N. Stamatopoulos, and G. Louloudis, IC- DAR2009 handwriting segmentation contest, International Journal on Document Analysis and Recognition (IJDAR), vol.141, pp.25-33, 2011.
DOI : 10.1007/s10032-010-0122-8

URL : http://www.cse.salford.ac.uk/prima/papers/ICDAR2007_HandwritingSegmentationCompetition.pdf

. Gatos, N. Basilis, G. Stamatopoulos, and . Louloudis, ICFHR 2010 Handwriting Segmentation Contest, 2010 12th International Conference on Frontiers in Handwriting Recognition, pp.737-742, 2010.
DOI : 10.1109/ICFHR.2010.120

F. A. Gers and E. Schmidhuber, LSTM recurrent networks learn simple context-free and context-sensitive languages, IEEE Transactions on Neural Networks 12.6, pp.1333-1340, 2001.
DOI : 10.1109/72.963769

D. Ghosh, T. Dube, and A. Shivaprasad, Script Recognition???A Review, IEEE Transactions on pattern analysis and machine intelligence 32.12, pp.2142-2161, 2010.
DOI : 10.1109/TPAMI.2010.30

A. Ghoshal, P. Swietojanski, and S. Renals, Multilingual training of deep neural networks, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.7319-7323, 2013.
DOI : 10.1109/ICASSP.2013.6639084

URL : http://homepages.inf.ed.ac.uk/s1136550/data/Ghoshal_ICASSP2013.pdf

I. J. Good, The population frequencies of species and the estimation of population parameters, Biometrika, pp.237-264, 1953.

J. T. Goodman, A bit of progress in language modeling, Computer Speech & Language 15.4, pp.403-434, 2001.
DOI : 10.1006/csla.2001.0174

N. Gorski, A2iA Check Reader: a family of bank check recognition systems, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), pp.523-526, 1999.
DOI : 10.1109/ICDAR.1999.791840

V. Govindan and A. Shivaprasad, Character recognition ??? A review, Pattern Recognition, vol.23, issue.7, pp.671-683, 1990.
DOI : 10.1016/0031-3203(90)90091-X

A. Graves, A. Mohamed, and G. Hinton, Speech recognition with deep recurrent neural networks, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6645-6649, 2013.
DOI : 10.1109/ICASSP.2013.6638947

URL : http://learning.cs.toronto.edu/~hinton/absps/RNN13.pdf

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks 18.5, pp.602-610, 2005.
DOI : 10.1016/j.neunet.2005.06.042

URL : http://www6.in.tum.de/pub/Main/Publications/Graves2005a.pdf

A. Graves, Connectionist temporal classification, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.369-376, 2006.
DOI : 10.1145/1143844.1143891

E. Grosicki and H. El-abed, ICDAR 2011 - French Handwriting Recognition Competition, 2011 International Conference on Document Analysis and Recognition, pp.1459-1463, 2011.
DOI : 10.1109/ICDAR.2011.290

E. Grosicki, Results of the RIMES Evaluation Campaign for Handwritten Mail Processing, 2009 10th International Conference on Document Analysis and Recognition, pp.941-945, 2009.
DOI : 10.1109/ICDAR.2009.224

D. Guillevic, Y. Ching, and . Suen, Recognition of legal amounts on bank cheques, Pattern Analysis and Applications, vol.16, issue.5, pp.28-41, 1998.
DOI : 10.1109/34.291440

K. Hacioglu and W. Ward, Dialog-context dependent language modeling combining n-grams and stochastic context-free grammars, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), pp.537-540, 2001.
DOI : 10.1109/ICASSP.2001.940886

URL : http://mti.xidian.edu.cn/multimedia/2001/supp/icassp2001/MAIN/papers/pap1270.pdf

M. Hamdani, A. El-desoky, H. Mousa, and . Ney, Open Vocabulary Arabic Handwriting Recognition Using Morphological Decomposition, 2013 12th International Conference on Document Analysis and Recognition, pp.280-284, 2013.
DOI : 10.1109/ICDAR.2013.63

URL : http://www-i6.informatik.rwth-aachen.de/publications/download/888/Hamdani-ICDAR-2013.pdf

M. Hindson, Free English language hyphenation dictionary, 2015.

G. Hinton, R. De-rumelhart, and . Williams, Learning internal representations by back-propagating errors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1, 1985.

S. Hochreiter, Untersuchungen zu dynamischen neuronalen Netzen, 1991.

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural computation 9.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

B. Hsu and . Paul, Language modeling for limited-data domains, 2009.

X. Huang, The SPHINX-II speech recognition system: an overview, Computer Speech & Language 7, pp.137-148, 1993.
DOI : 10.1006/csla.1993.1007

X. Huang, Spoken language processing: A guide to theory, algorithm , and system development, 2001.

N. Indurkhya and F. J. Damerau, Handbook of natural language processing, 2010.

F. Jelinek, SELF-ORGANIZED LANGUAGE MODELING FOR SPEECH RECOGNITION, Readings in speech recognition, pp.450-506, 1990.
DOI : 10.1016/B978-0-08-051584-7.50045-0

URL : http://www.aclweb.org/anthology-new/H/H91/H91-1057.pdf

F. Jelinek, A dynamic language model for speech recognition, Proceedings of the workshop on Speech and Natural Language , HLT '91, pp.293-295, 1991.
DOI : 10.3115/112405.112464

Y. Jia, Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.675-678, 2014.
DOI : 10.1145/2647868.2654889

S. Johansson, The LOB corpus of British English texts: presentation and comments, In: ALLC journal, vol.1, issue.1, pp.25-36, 1980.

S. Jonas, Improved modeling in handwriting recognition " . In: Master's thesis, Human Language Technology and Pattern Recognition Group, 2009.

R. Jones, S. James, . Downey, S. John, and . Mason, Continuous speech recognition using syllables, 1997.

T. Joshua and J. Goodman, A bit of progress in language modeling extended version, Machine Learning and Applied Statistics Group Microsoft Research, 2001.

D. Jurafsky, Using a stochastic context-free grammar as a language model for speech recognition, 1995 International Conference on Acoustics, Speech, and Signal Processing, pp.189-192, 1995.
DOI : 10.1109/ICASSP.1995.479396

A. Kaltenmeier, Sophisticated topology of hidden Markov models for cursive script recognition, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93), pp.139-142, 1993.
DOI : 10.1109/ICDAR.1993.395764

V. Katsouros and V. Papavassiliou, Segmentation of Handwritten Document Images into Text Lines, 2011.
DOI : 10.5772/15923

URL : http://cdn.intechopen.com/pdfs/15381/InTech-Segmentation_of_handwritten_document_images_into_text_lines.pdf

Y. Kessentini, T. Paquet, and A. Benhamadou, A multi-stream HMM-based approach for off-line multi-script handwritten word recognition, p.1, 2008.
DOI : 10.1109/icdar.2007.4378724

F. Kimura, M. Shridhar, and Z. Chen, 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.18-22, 1993.
DOI : 10.1109/ICDAR.1993.395791

D. Klakow and J. Peters, Testing the correlation of word error rate and perplexity, Speech Communication, vol.38, issue.1-2, pp.19-28, 2002.
DOI : 10.1016/S0167-6393(01)00041-3

R. Kneser and H. Ney, Improved backing-off for mgram language modeling, Acoustics, Speech, and Signal Processing, pp.181-184, 1995.
DOI : 10.1109/icassp.1995.479394

A. L. Koerich, R. Sabourin, Y. Ching, and . Suen, Large vocabulary off-line handwriting recognition: A survey, Pattern Analysis & Applications 6.2, pp.97-121, 2003.
DOI : 10.1007/s10044-002-0169-3

M. Kozielski, P. Doetsch, and H. Ney, Improvements in rwth's system for off-line handwriting recognition, Document Analysis and Recognition (ICDAR), 2013 12th International Conference on. IEEE, pp.935-939, 2013.

M. Kozielski, Multilingual Off-Line Handwriting Recognition in Real-World Images, 2014 11th IAPR International Workshop on Document Analysis Systems, pp.121-125, 2014.
DOI : 10.1109/DAS.2014.8

M. Kozielski, Open-Lexicon Language Modeling Combining Word and Character Levels, 2014 14th International Conference on Frontiers in Handwriting Recognition, pp.343-348, 2014.
DOI : 10.1109/ICFHR.2014.64

URL : http://www-i6.informatik.rwth-aachen.de/publications/download/931/Kozielski-ICFHR-2014.pdf

T. Kuhn, H. Niemann, and E. Günter-schukat-talamazzini, Ergodic hidden Markov models and polygrams for language modeling, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, 1994.
DOI : 10.1109/ICASSP.1994.389282

URL : http://www5.informatik.uni-erlangen.de/literature/ps-dir/1994/Kuhn94:EHM.ps.gz

K. Kukich, Technique for automatically correcting words in text, ACM Computing Surveys (CSUR) 24.4, pp.377-439, 1992.
DOI : 10.1145/146370.146380

L. Cun, L. Yann, Y. Bottou, and . Bengio, Reading checks with multilayer graph transformer networks, Acoustics, Speech, and Signal Processing IEEE International Conference on, pp.151-154, 1997.

P. Lecocq, Apprentissage de la lecture et dyslexie, 1991.

Y. Lecun, Backpropagation Applied to Handwritten Zip Code Recognition, Neural computation 1.4, pp.541-551, 1989.
DOI : 10.1007/BF00133697

A. Lee, T. Kawahara, and K. Shikano, Julius?an open source real-time large vocabulary recognition engine, 2001.

J. J. Lee, H. Jin, and . Kim, A unified network-based approach for online recognition of multi-lingual cursive handwritings, 1997.

. Lee, Y. Seong-whan, and . Kim, A new type of recurrent neural network for handwritten character recognition, Document Analysis and Recognition Proceedings of the Third International Conference on, pp.38-41, 1995.

V. I. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, In: Soviet physics doklady, vol.10, p.707, 1966.

Y. Li, Script-independent text line segmentation in freestyle handwritten documents, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.308, pp.1313-1329, 2008.
DOI : 10.21236/ada460371

URL : http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA460371&Location=U2&doc=GetTRDoc.pdf

S. Madhvanath and V. Govindaraju, Holistic lexicon reduction for handwritten word recognition, Electronic Imaging: Science & Technology. International Society for Optics and Photonics, pp.224-234, 1996.
DOI : 10.1117/12.234704

S. Madhvanath and V. Krpasundar, Pruning large lexicons using generalized word shape descriptors, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.552-555, 1997.
DOI : 10.1109/ICDAR.1997.620561

P. Majewski, Syllable Based Language Model for Large Vocabulary Continuous Speech Recognition of Polish, International Conference on Text, Speech and Dialogue, pp.397-401, 2008.
DOI : 10.1007/978-3-540-87391-4_51

A. Malaviya, C. Leja, and L. Peters, Multi-script handwriting recognition with FOHDEL, Proceedings of North American Fuzzy Information Processing, pp.147-151, 1996.
DOI : 10.1109/NAFIPS.1996.534720

URL : http://www.malaviya.com/~ashutosh/fraunhofer/nafips96.pdf

C. D. Manning and H. Schütze, Foundations of statistical natural language processing, 1999.

J. Mantas, An overview of character recognition methodologies, Pattern Recognition, vol.19, issue.6, pp.425-430, 1986.
DOI : 10.1016/0031-3203(86)90040-3

S. Mao, T. Rosenfeld, and . Kanungo, Document structure analysis algorithms: a literature survey, Electronic Imaging 2003. International Society for Optics and Photonics, pp.197-207, 2003.
DOI : 10.1117/12.476326

URL : http://lhncbc.nlm.nih.gov:80/lhc/docs/published/2003/pub2003015.pdf

V. Märgner and . Abed, Guide to OCR for Arabic scripts, 2012.
DOI : 10.1007/978-1-4471-4072-6

U. Marti and H. Bunke, Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system, International journal of Pattern Recognition and Artificial, pp.65-90, 2001.

W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity " . In: The bulletin of mathematical biophysics 5, pp.115-133, 1943.

Y. Miao, M. Gowayyed, and F. Metze, EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.2015-167, 2015.
DOI : 10.1109/ASRU.2015.7404790

URL : http://arxiv.org/pdf/1507.08240

T. Mikolov, Statistical language models based on neural networks, Presentation at Google, Mountain View, 2012.

T. Mikolov, Recurrent neural network based language model, In: Interspeech, vol.2, p.3, 2010.

T. Mikolov, Extensions of recurrent neural network language model, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5528-5531, 2011.
DOI : 10.1109/ICASSP.2011.5947611

T. Mikolov, Rnnlm-recurrent neural network language modeling toolkit, Proc. of the 2011 ASRU Workshop, pp.196-201, 2011.

T. Mikolov, Subword language modeling with neural networks " . In: preprint (http, 2012.

M. Mohri, Finite-state transducers in language and speech processing, Computational linguistics 23.2, pp.269-311, 1997.

M. Mohri, F. Pereira, and M. Riley, Weighted finite-state transducers in speech recognition, Computer Speech & Language, vol.16, issue.1, pp.69-88, 2002.
DOI : 10.1006/csla.2001.0184

URL : http://www.cs.toronto.edu/~gpenn/csc2518/mohri-pereira-riley02.pdf

S. Mori, K. Yamamoto, and M. Yasuda, Research on Machine Recognition of Handprinted Characters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, issue.4, pp.386-405, 1984.
DOI : 10.1109/TPAMI.1984.4767545

A. Mousa and . El-desoky, Sub-lexical language models for German LVCSR, Spoken Language Technology Workshop (SLT), pp.171-176, 2010.

A. I. Mousa, H. El-desoky, and . Ney, Sub-word based language modeling of morphologically rich languages for LVCSR, 2014.

B. Moysset, The A2iA Multi-lingual Text Recognition System at the Second Maurdor Evaluation, 2014 14th International Conference on Frontiers in Handwriting Recognition, pp.297-302, 2014.
DOI : 10.1109/ICFHR.2014.57

B. New, Lexique 2 : A new French lexical database, Behavior Research Methods, Instruments, & Computers, vol.103, issue.3, pp.516-524, 2004.
DOI : 10.3406/psy.2003.29626

URL : https://link.springer.com/content/pdf/10.3758%2FBF03195598.pdf

W. Pantke, HADARA?A software system for semi-automatic processing of historical handwritten Arabic documents, Archiving Conference . Society for Imaging Science and Technology, vol.2013, issue.1, pp.161-166, 2013.

V. Papavassiliou, Handwritten document image segmentation into text lines and words, Pattern Recognition, vol.43, issue.1, pp.369-377, 2010.
DOI : 10.1016/j.patcog.2009.05.007

T. Paquet and Y. Lecourtier, Automatic reading of the literal amount of bank checks, Machine Vision and Applications 6.2-3, pp.151-162, 1993.
DOI : 10.1007/BF01211938

V. Pham, Dropout Improves Recurrent Neural Networks for Handwriting Recognition, 2014 14th International Conference on Frontiers in Handwriting Recognition, pp.285-290, 2014.
DOI : 10.1109/ICFHR.2014.55

URL : http://arxiv.org/pdf/1312.4569

T. Plötz, A. Gernot, and . Fink, Markov models for offline handwriting recognition: a survey, IJDAR) 12.4, pp.269-298, 2009.
DOI : 10.1109/MC.2007.314

D. Povey, The Kaldi speech recognition toolkit, IEEE 2011 workshop on automatic speech recognition and understanding. EPFL-CONF- 192584, 2011.

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE 77.2, pp.257-286, 1989.

C. Ramisch, N-gram models for language detection, 2008.

R. Ridouane, Y. Meynadier, and C. Fougeron, La syllabe: objet théorique et réalité physique, pp.225-246, 2011.

K. Ries, Y. Finn-dag-buo, and . Wang, Improved language modelling by unsupervised acquisition of structure, 1995 International Conference on Acoustics, Speech, and Signal Processing, pp.193-196, 1995.
DOI : 10.1109/ICASSP.1995.479397

B. D. Ripley, Statistical aspects of neural networks, pp.40-123, 1993.
DOI : 10.1007/978-1-4899-3099-6_2

A. J. Robinson, An application of recurrent nets to phone probability estimation, IEEE transactions on Neural Networks 5.2, pp.298-305, 1994.
DOI : 10.1109/72.279192

S. Roekhaut, S. Brognaux, and R. Beaufort, Syllabation graphémique automatique à l'aide d'un dictionnaire phonétique aligné, 2012.

V. Romero, J. Andreu, and E. Vidal, Handwritten Text Recognition for Marriage Register Books, 2011 International Conference on Document Analysis and Recognition, pp.533-537, 2011.
DOI : 10.1109/ICDAR.2011.113

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, p.386, 1958.
DOI : 10.1037/h0042519

R. Rosenfeld, . Rumelhart, E. David, E. Geoffrey, R. J. Hinton et al., Two decades of statistical language modeling: Where do we go from here Learning representations by back-propagating errors, p.1, 1988.

D. Rybach, The RWTH aachen university open source speech recognition system, pp.2111-2114, 2009.

E. Ryst, Syllabation en anglais et en français: considérations formelles et expérimentales, 2014.

Y. Sagisaka and N. Iwahashi, Objective optimization in algorithms for text-to-speech synthesis " . In: Speech Coding and Synthesis, pp.686-706, 1995.

V. Sahu, B. Laxmi, and . Kubde, Offline Handwritten Character Recognition Techniques using Neural Network: A Review, International Journal of Science and Research (IJSR), pp.2319-7064, 2013.

J. Sánchez and . Andreu, tranScriptorium, Proceedings of the 2013 ACM symposium on Document engineering, DocEng '13, pp.227-228, 2013.
DOI : 10.1145/2494266.2494294

M. Schambach, J. Rottland, and T. Alary, How to convert a Latin handwriting recognition system to Arabic, Proceedings of the international conference on frontiers in handwriting recognition, 2008.

C. Schrumpf, M. Larson, and S. Eickeler, Syllable-based language models in speech recognition for English spoken document retrieval, Proc. of the 7th International Workshop of the EU Network of Excellence DELOS on AVIVDiLib, pp.196-205, 2005.

E. Schukat-talamazzini and . Günter, Permugram language models, 1995.

M. Schuster, K. Kuldip, and . Paliwal, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, vol.45, issue.11, pp.2673-2681, 1997.
DOI : 10.1109/78.650093

URL : https://maxwell.ict.griffith.edu.au/spl/publications/papers/ieeesp97_schuster.pdf

A. W. Senior, J. Anthony, and . Robinson, An off-line cursive handwriting recognition system, IEEE transactions on pattern analysis and machine intelligence 20.3, pp.309-321, 1998.
DOI : 10.1109/34.667887

A. Senior and . William, Off-line cursive handwriting recognition using recurrent neural networks, 1994.

A. Sethy, B. Ramabhadran, and S. Narayanan, Improvements in English ASR for the MALACH project using syllable-centric models, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), pp.129-134, 2003.
DOI : 10.1109/ASRU.2003.1318416

A. Shaik and . Basha, Hybrid Language Models Using Mixed Types of Sub-Lexical Units for Open Vocabulary German LVCSR, In: INTER- SPEECH, pp.1441-1444, 2011.

C. Shannon and . Elwood, A mathematical theory of communication, The Bell System Technical Journa 5.1, pp.3-55, 1948.

Z. Shi, S. Setlur, and V. Govindaraju, A steerable directional local profile technique for extraction of handwritten arabic text lines IAM Handwriting Database, site, IAM official, pp.176-18006, 2009.

M. Siu and M. Ostendorf, Variable n-grams and extensions for conversational speech language modeling, IEEE Transactions on Speech and Audio Processing 8.1, pp.63-75, 2000.

. Srihari and N. Sargur, Handwritten address interpretation: a task of many pattern recognition problems " . In: International journal of pattern recognition and artificial intelligence 14, pp.5-663, 2000.

. Srihari, N. Sargur, J. Edward, and . Kuebert, Integration of hand-written address interpretation technology into the United States Postal Service Remote Computer Reader system, Proceedings of the Fourth International Conference on Document Analysis and Recognition, pp.892-896, 1997.
DOI : 10.1109/ICDAR.1997.620640

N. Stamatopoulos, ICDAR 2013 Handwriting Segmentation Contest, 2013 12th International Conference on Document Analysis and Recognition, pp.1402-1406, 2013.
DOI : 10.1109/ICDAR.2013.283

URL : http://www.cse.salford.ac.uk/prima/papers/ICDAR2007_HandwritingSegmentationCompetition.pdf

A. Stolcke, SRILM-an extensible language modeling toolkit, In: Interspeech, 2002.

C. Y. Suen, M. Berthod, and S. Mori, Automatic recognition of handprinted characters???The state of the art, Proceedings of the IEEE 68.4, pp.469-487, 1980.
DOI : 10.1109/PROC.1980.11675

C. Sutton and A. Mccallum, An introduction to conditional random fields for relational learning, Introduction to statistical relational learning, pp.93-128, 2006.
DOI : 10.1561/2200000013

URL : http://www.cs.umass.edu/%7Ecasutton/publications/crftut-fnt.pdf

S. Wassim, J. Lerouge, and T. Paquet, Unified French / English syllabic model for handwriting recognition, Handwriting Recognition (ICFHR), 2016 15th International Conference on Frontiers in Handwriting Recognition. under submission, 2016.

W. Swaileh, J. Lerouge, and T. Paquet, A Unified French/English Syllabic Model for Handwriting Recognition, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.536-541, 2016.
DOI : 10.1109/ICFHR.2016.0104

W. Swaileh and T. Paquet, A syllable based model for handwriting recognition, Pattern Recognition (ICPR), 2016 23rd International Conference on Pattern Recognition. under submission, 2016.

W. Swaileh, Handwriting Recognition with Multigrams, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017.
DOI : 10.1109/ICDAR.2017.31

C. Szegedy, Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
DOI : 10.1109/CVPR.2015.7298594

URL : http://arxiv.org/pdf/1409.4842

. Tang, Y. Yuan, . Seong-whan, . Lee, Y. Ching et al., Automatic document processing: a survey " . In: Pattern recognition 29, pp.1931-1952, 1996.
DOI : 10.1016/s0031-3203(96)00044-1

A. Toselli and . Hector, INTEGRATED HANDWRITING RECOGNITION AND INTERPRETATION USING FINITE-STATE MODELS, International Journal of Pattern Recognition and Artificial Intelligence, vol.18, issue.04, pp.519-539, 2004.
DOI : 10.1016/S0031-3203(98)00081-8

B. Van-merriënboer, Blocks and fuel: Frameworks for deep learning, 2015.

P. Voigtlaender, P. Doetsch, and H. Ney, Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp.228-233, 2016.
DOI : 10.1109/ICFHR.2016.0052

W. Walker, Sphinx-4: A flexible open source framework for speech recognition, 2004.

S. Wang and . Xinlei, Using graphone models in automatic speech recognition, 2009.

S. Wiesler, RASR/NN: The RWTH neural network toolkit for speech recognition, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3281-3285, 2014.
DOI : 10.1109/ICASSP.2014.6854207

URL : http://www-i6.informatik.rwth-aachen.de/publications/download/904/Wiesler-ICASSP-2014.pdf

B. Xu, Speaker-independent dictation of Chinese speech with 32K vocabulary " . In: Spoken Language, ICSLP 96. Proceedings., Fourth International Conference on, pp.2320-2323, 1996.

S. J. Young and S. Young, The HTK hidden Markov model toolkit: Design and philosophy, 1993.

S. Yousfi, S. Berrani, and C. Garcia, Contribution of recurrent connectionist language models in improving LSTM-based Arabic text recognition in videos, Pattern Recognition, vol.64, pp.245-254, 2017.
DOI : 10.1016/j.patcog.2016.11.011

URL : https://hal.archives-ouvertes.fr/hal-01413629

S. Yu, Hidden semi-Markov models, Artificial Intelligence, vol.174, issue.2, pp.215-243, 2010.
DOI : 10.1016/j.artint.2009.11.011