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Application of Bidirectional Probabilistic Character Language Model in Handwritten Words Recognition

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

This paper presents a concept of bidirectional probabilistic character language model and its application to handwriting recognition. Character language model describes probability distribution of adjacent character combinations in words. Bidirectional model applies word analysis from left to right and in reversed order, i.e. it uses conditional probabilities of character succession and character precedence. Character model is used for HMM creation, which is applied as a soft word classifier. Two HMMs are created for left-to-right and right-to-left analysis. Final word classification is obtained as a combination of unidirectional recognitions. Experiments carried out with medical texts recognition revealed the superiority of combined classifier over its components.

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References

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Author information

Authors and Affiliations

  1. Institute of Applied Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Jerzy Sas

Authors
  1. Jerzy Sas
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Sas, J. (2006). Application of Bidirectional Probabilistic Character Language Model in Handwritten Words Recognition. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_82

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  • DOI: https://doi.org/10.1007/11875581_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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