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Combining Character Level Classifier and Probabilistic Lexicons in Handwritten Word Recognition – Comparative Analysis of Methods

  • Marek Kurzynski
  • Jerzy Sas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3691)

Abstract

In this paper the probabilistic aproach to handwritten words recognition is described. The decision is performed using results of character classification based on a character image analysis and probabilistic lexicon treated as a special kind of soft classifier. The novel approach to combining these both classifiers is proposed, where fusion procedure interleaves soft outcomes of both classifiers so as to obtain the best recognition quality. The proposed algorithms were experimentally investigated and results of recognition of polish handwritten surnames and names are given.

Keywords

Word Recognition Character Recognition Handwritten Character Handwritten Document Handwritten Digit Recognition 
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

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

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