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)


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.


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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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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