Abstract
Handwritten word recognition has received a substantial amount of attention in the past. Neural Networks as well as discriminatively trained Maximum Margin Hidden Markov Models have emerged as cutting-edge alternatives to the commonly used Hidden Markov Models. In this paper, we analyze the combination of these classifiers with respect to their potential for improving recognition performance. It is shown that a significant improvement can in fact be achieved, although the individual recognizers are highly optimized state-of-the-art systems. Also, it is demonstrated that the diversity of the recognizers has a profound impact on the improvement that can be achieved by the combination.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vinciarelli, A.: A Survey On Off-Line Cursive Word Recognition. Pattern Recognition 35(7), 1433–1446 (2002)
Brakensiek, A., Rigoll, G.: Handwritten Address Recognition Using Hidden Markov Models. In: Dengel, A.R., Junker, M., Weisbecker, A. (eds.) Reading and Learning. LNCS, vol. 2956, pp. 103–122. Springer, Heidelberg (2004)
Kim, G., Govindaraju, V., Srihari, S.N.: An architecture for handwritten text recognition systems. Int’l Journal on Document Analysis and Recognition 2(1), 37–44 (1999)
Do, T.-M.-T., Artieres, T.: Maximum Margin Training of Gaussian HMMs for Handwriting Recognition. In: 10th Int’l Conference on Document Analysis and Recognition (2009)
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist Temporal Classification: Labelling Unsegmented Sequential Data with Recurrent Neural Networks. In: 23rd Int’l Conf. on Machine Learning, pp. 369–376 (2006)
Kittler, J., Roli, F. (eds.): MCS 2000. LNCS, vol. 1857. Springer, Heidelberg (2000)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
Günter, S., Bunke, H.: Ensembles of Classifiers for Handwritten Word Recognition. Int’l Journal on Document Analysis and Recognition 5(4), 224–232 (2003)
Verma, B.K., Gader, P.D., Chen, W.-T.: Fusion of multiple handwritten word recognition techniques. Pattern Recognition Letters 22(9), 991–998 (2001)
Wang, W., Brakensiek, A., Rigoll, G.: Combination of Multiple Classifiers for Handwritten Word Recognition. In: Int’l Workshop on Frontiers in Handwriting Recognition, pp. 117–122 (2002)
Marti, U.-V., Bunke, H.: The IAM-Database: An English Sentence Database for Offline Handwriting Recognition. Int’l Journal on Document Analysis and Recognition 5, 39–46 (2002)
Marti, U.-V., Bunke, H.: Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System. Int’l Journal of Pattern Recognition and Artificial Intelligence 15, 65–90 (2001)
Yu, D., Deng, L.: Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition. In: First IEEE Int’l Conference on Semantic Computing, pp. 429–438 (2007)
Sha, F., Saul, L.K.: Large Margin Hidden Markov Models for Automatic Speech Recognition. In: NIPS. MIT Press, Cambridge (2007)
Do, T.-M.-T., Artieres, T.: Large Margin Trainng for Hidden Markov Models with Partially Observed States. In: 26th Int’l Conference on Machine Learning (2009)
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence (accepted for publication)
Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51(2), 181–207 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Frinken, V., Peter, T., Fischer, A., Bunke, H., Do, TMT., Artieres, T. (2009). Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-03767-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
eBook Packages: Computer ScienceComputer Science (R0)