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Application of Statistic Properties of Letters Succession in Polish Language to Handprint Recognition

  • Jerzy Sas
  • Marek Kurzynski
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)

Abstract

In the paper the method of handprinted word recognition is described, which combines statistical lexical language model and character classifier properties in order to improve the recognition accuracy. The statistical lexical model determines the conditional probabilities of letters succession in the language. For some letters in polish language only very small subset of successors appears with significant conditional probability. If the confidence of predecessor recognition is assessed as high then the recognition of successor can be reliably supported by utilizing probabilistic lexical properties. In contrast to many other approaches, the method is not based on lexicons, so it can be used in these cases where the exhaustive lexicon is not available or its usage is inefficient, e.g. due to great number of elements.

Keywords

Word Recognition Character Classifier Support Factor Handwriting Recognition Handwritten Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Marti U.V., Bunke H. (2001) 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, Vol 15, No 1: 65–90CrossRefGoogle Scholar
  2. 2.
    Bote-Lorenzo M.L., Dimitriadis Y.A., Gomez-Sanchez E. (2003) Automatic Extraction of Human-recognizable Shape and Execution Prototypes of Handwritten Characters, Pattern Recognition, Vol 36: 1605–1617CrossRefGoogle Scholar
  3. 3.
    Hanmandlu M., Murali Mohan K.R. (2003) Unconstrained Handwritten Character Recognition Based on Fuzzy Logic, Pattern Recognition, Vol 36: 603–623CrossRefGoogle Scholar
  4. 4.
    Koerich A. L., Sabourin R., Suen C.Y. (2003) Large Vocabulary off-line Handwriting Recognition: A Survey, Pattern Anal. Aplic., No 6: 97–121CrossRefMathSciNetGoogle Scholar
  5. 5.
    Liu C., Nakashima K., Sako H., Fujisawa H. (2003) Handwritten Digit Recognition: Benchmarking of State-of-the-art Techniques, Pattern Recognition, Vol. 36: 2271–2285zbMATHCrossRefGoogle Scholar
  6. 6.
    Sas J., Kurzynski M. (2005) Multilevel Recognition of Structured Handprinted Documents-Probabilistic Approach, Proc. Int. Conf. on Computer Recognition Systems CORES’05, Springer Verlag (in this Volume)Google Scholar
  7. 7.
    Sas J. (2004) Handwritten Laboratory Test Order Form Recognition Module For Distributed Clinic, Journ. of Medical Informatics & Technologies, Vol 8: 59–68Google Scholar
  8. 8.
    Sas J. (2004) Three-Level, Lexicon-Based Handwritten Form Recognition Method, In: Klopotek M., Tchorzewski J. (eds) Proc, of VI Int. Conf on Artificial Intelligence AI-19’2004, Vol. 1(23): 113–124Google Scholar
  9. 9.
    Vinciarelli A, Bengio S, Bunke H (2004) Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models, IEEE Trans. on Pattern Anal. and Machine Intelligence, Vol 26, No 6: 709–720CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jerzy Sas
    • 1
  • Marek Kurzynski
    • 2
  1. 1.Institute of Applied InformaticsWroclaw University of TechnologyWroclawPoland
  2. 2.Faculty of Electronics, Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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