Article

Multiple classifier methods for offline handwritten text line recognition

Authors:

Roman Bertolami,

Horst BunkeAuthors Info & Claims

MCS'07: Proceedings of the 7th international conference on Multiple classifier systems

Pages 72 - 81

Published: 23 May 2007 Publication History

Abstract

This paper investigates the use of multiple classifier methods for offline handwritten text line recognition. To obtain ensembles of recognisers we implement a random feature subspace method. The word sequences returned by the individual ensemble members are first aligned. Then the final word sequence is produced. For this purpose we use a voting method and two novel statistical combination methods. The conducted experiments show that the proposed multiple classifier methods have the potential to improve the recognition accuracy of single recognisers.

References

[1]

Kim, G., Govindaraju, V., Srihari, S.: Architecture for handwritten text recognition systems. In Lee, S.W., ed.: Advances in Handwriting Recognition, World Scientific Publ. Co. (1999) 163-172.

[2]

Senior, A., Robinson, A.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence <b>20</b>(3) (1998) 309-321.

Digital Library

[3]

Vinciarelli, A., Bengio, S., Bunke, H.: Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence <b>26</b>(6) (2004) 709-720.

Digital Library

[4]

Zimmermann, M., Chappelier, J.C., Bunke, H.: Offline grammar-based recognition of handwritten sentences. IEEE Transactions on Pattern Analysis and Machine Intelligence <b>28</b>(5) (2006) 818-821.

Digital Library

[5]

Rahman, A., Fairhurst, M.: Multiple classifier decision combination strategies for character recognition: A review. International Journal on Document Analysis and Recognition <b>5</b>(4) (2003) 166-194.

[6]

Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons Inc (2004).

Digital Library

[7]

Oza, N., Polikar, R., Kittler, J., Roli, F., eds.: Multiple Classifier Systems, 6th International Workshop, Springer LNCS 3541 (2005).

Digital Library

[8]

Huang, Y.S. Suen, C.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence <b>17</b>(1) (1995) 90-94.

Digital Library

[9]

Oliveira, L.S., Morita, M., Sabourin, R.: Feature selection for ensembles applied to handwriting recognition. International Journal on Document Analysis and Recognition <b>8</b>(4) (2006) 262-279.

[10]

Sirlantzkis, K., Fairhurst, M., Hoque, M.: Genetic algorithm for multiple classifier configuration: A case study in character recognition. In: 2nd International Workshop on Multiple Classifier Systems, Cambridge, England. (2001) 99-108.

Digital Library

[11]

Gader, P., Mohamed, M., Keller, J.: Fusion of handwritten word classifiers. Pattern Recognition Letters <b>17</b> (1996) 577-584.

Digital Library

[12]

Günter, S., Bunke, H.: Multiple classifier systems in off-line handwritten word recognition - on the influence of training set and vocabulary size. International Journal of Pattern Recognition and Artificial Intelligence <b>18</b>(7) (2004) 1303-1320.

[13]

Marti, U.V., Bunke, H.: Use of positional information in sequence alignment for multiple classifier combination. In: 2nd International Workshop on Multiple Classifier Systems, Cambridge, England. (2001) 388-398.

Digital Library

[14]

Bertolami, R., Bunke, H.: Ensemble methods for handwritten text line recognition systems. In: International Conference on Systems, Man and Cybernetics, Hawaii, USA. (2005) 2334-2339.

[15]

Fiscus, J.: A post-processing system to yield reduced word error rates: Recognizer output voting error reduction. In: IEEE Workshop on Automatic Speech Recognition and Understanding, Santa Barbara. (1997) 347-352.

[16]

Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. International Journal of Pattern Recognition and Artificial Intelligence <b>15</b> (2001) 65-90.

[17]

Brakensiek, A., Rigoll, G.: Handwritten address recognition using hidden Markov models. In Dengel, A., Junker, M., Weisbecker, A., eds.: Reading and Learning, Springer (2004) 103-122.

[18]

Ho, T.K.: The random space method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence <b>20</b>(8) (1998) 832-844.

Digital Library

[19]

Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics <b>22</b>(3) (1992) 418-435.

[20]

Raudys, S.: Trainable fusion rules. i. large sample size case. Neural Networks <b>19</b>(10) (1006) 1506-1516.

Digital Library

[21]

Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition <b>5</b> (2002) 39-46.

[22]

Johansson, S., Atwell, E., Garside, R., Leech, G.: The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities, Bergen, Norway. (1986).

[23]

Francis, W.N., Kucera, H.: Brown corpus manual, Manual of Information to accompany A Standard Corpus of Present-Day Edited American English, for use with Digital Computers. Department of Linguistics, Brown University, Providence, USA (1979).

[24]

Bauer, L.: Manual of Information to Accompany TheWellington Corpus of Written New Zealand English. Department of Linguistics, Victoria University, Wellington, New Zealand (1993).

[25]

Goodman, J.: A bit of progress in language modeling. Technical Report MSR-TR- 2001-72, Microsoft Research (2001).

Digital Library

Cited By

Bertolami RBunke H(2008)Hidden Markov model-based ensemble methods for offline handwritten text line recognitionPattern Recognition10.1016/j.patcog.2008.04.00341:11(3452-3460)Online publication date: 1-Nov-2008
https://dl.acm.org/doi/10.1016/j.patcog.2008.04.003
Bertolami RBunke H(2006)Ensemble methods to improve the performance of an English handwritten text line recognizerProceedings of the 2006 conference on Arabic and Chinese handwriting recognition10.5555/1792262.1792278(265-277)Online publication date: 27-Sep-2006
https://dl.acm.org/doi/10.5555/1792262.1792278

Multiple classifier methods for offline handwritten text line recognition
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Recommendations

Offline arabic handwritten text recognition: A Survey

Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features ...
A Bayesian-based method of unconstrained handwritten offline Chinese text line recognition

This paper presents a new Bayesian-based method of unconstrained handwritten offline Chinese text line recognition. In this method, a sample of a real character or non-character in realistic handwritten text lines is jointly recognized by a traditional ...
Offline recognition of handwritten Bangla characters: an efficient two-stage approach

The present work deals with recognition of handwritten characters of Bangla, a major script of the Indian sub-continent. The main contributions presented here are (a) generation of a database of handwritten basic characters of Bangla and (b) development ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

MCS'07: Proceedings of the 7th international conference on Multiple classifier systems

May 2007

524 pages

ISBN:9783540724810

Editors:
Michal Haindl
Academy of Sciences CR, Institute of Information Theory and Automation, Prague, Czech Republic
,
Josef Kittler
University of Surrey, Centre for Vision, Speech and Signal Processing, Guildford, Surrey, UK
,
Fabio Roli
University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, Italy

Sponsors

EU IST FP6 BioSecure Network of Excellence
EU IST FP6 MUSCLE Network of Excellence
IAPR: International Association for Pattern Recognition
University of Surrey
University of Cagliari: University of Cagliari

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 May 2007

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bertolami RBunke H(2008)Hidden Markov model-based ensemble methods for offline handwritten text line recognitionPattern Recognition10.1016/j.patcog.2008.04.00341:11(3452-3460)Online publication date: 1-Nov-2008
https://dl.acm.org/doi/10.1016/j.patcog.2008.04.003
Bertolami RBunke H(2006)Ensemble methods to improve the performance of an English handwritten text line recognizerProceedings of the 2006 conference on Arabic and Chinese handwriting recognition10.5555/1792262.1792278(265-277)Online publication date: 27-Sep-2006
https://dl.acm.org/doi/10.5555/1792262.1792278

View Options

View options

Figures

Tables

Media

View Table of Conten