Abstract
Handwriting recognition is challenging because of the inherent variability of character shapes. Popular approaches for handwriting recognition are markovian and neuronal. Both approaches can take as input, sequences of frames obtained by sliding a window along a word or a text-line. We present markovian (Dynamic Bayesian Networks, Hidden Markov Models) and recurrent neural network-based approaches (RNNs) dedicated to character, word and text-line recognition. These approaches are applied to the recognition of both Latin and Arabic scripts.
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References
Vinciarelli, A.: Online and offline handwriting recognition: A comprehensive survey. Pattern Recognition 35(7), 1433–1446 (2002)
Plamondon, R., Srihari, S.: Online and offline handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)
Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5) (2009)
Likforman-Sulem, L., Sigelle, M.: Recognition of degraded characters using dynamic bayesian networks. Pattern Recognition 41(10), 3092–3103 (2008)
Murphy, K.: BayesNet Toolbox for Matlab (2003), http://www.ai.mit.edu/~murphyk/Bayes/bnintro.html
Likforman-Sulem, L., Sigelle, M.: ENST ancient character database (2008), http://perso.telecom-paristech.fr/~lauli/ENST_ANCIENT_CHAR/
Al-Hajj-Mohamad, R., Likforman-Sulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved HMM-based arabic handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(7), 1165–1177 (2009)
Bianne-Bernard, A.L., Menasri, F., El-Hajj, R., Mokbel, C., Kermorvant, C., Likforman-Sulem, L.: Dynamic and contextual information in HMM modeling for handwritten word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(10) (2011)
Morillot, O., Likforman-Sulem, L., Grosicki, E.: New baseline correction algorithm for text-line recognition with bi-directional recurrent neural networks (2013)
Morillot, O., Likforman-Sulem, L., Grosicki, E.: Construction of language models for an handwritten mail reading system. In: Proceedings of Electronic Imaging - Document Recognition and Retrieval (DRR) XIX (2012)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: IFN/ENIT- database of handwritten arabic words. In: Colloque International Francophone sur l’Ecrit et le Document (2002)
Märgner, V., El-Abed, H.: ICDAR 2009 Arabic Handwriting Recognition Competition. In: International Conference on Document Analysis and Recognition (2009)
Grosicki, E., El-Abed, H.: ICDAR 2009 handwriting recognition competition. In: International Conference on Document Analysis and Recognition, pp. 1398–1402 (2009)
Grosicki, E., El-Abed, H.: ICDAR 2011: French handwriting recognition competition. In: International Conference on Document Analysis and Recognition (2011)
NIST: OpenHart 2013 Evaluation (2013), http://www.nist.gov/itl/iad/mig/hart2013.cfm
Breuel, T.: OCRopus - Open Source Document Analysis and OCR system (2013), https://code.google.com/p/ocropus
Graves, A.: RNNlib (2009), http://www6.in.tum.de/Main/Graves
Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Neural Information Processing Systems, pp. 545–552 (2008)
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Likforman-Sulem, L. (2014). Recent Approaches in Handwriting Recognition with Markovian Modelling and Recurrent Neural Networks. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_26
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DOI: https://doi.org/10.1007/978-3-319-04129-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
Online ISBN: 978-3-319-04129-2
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