A Hybrid Two-Stage Fuzzy ARTMAP and LVQ Neuro-fuzzy System for Online Handwriting Recognition

  • Miguel L. Bote-Lorenzo
  • Yannis A. Dimitriadis
  • Eduardo Gómez-Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)


This paper presents a two-stage handwriting recognizer for classification of isolated characters that exploits explicit knowledge on characters’ shapes and execution plans. The first stage performs prototype extraction of the training data using a Fuzzy ARTMAP based method. These prototypes are able to improve the performance of the second stage consisting of LVQ codebooks by means of providing the aforementioned explicit knowledge on shapes and execution plans. The proposed recognizer has been tested on the UNIPEN international database achieving an average recognition rate of 90.15%, comparable to that reached by humans and other recognizers found in literature.


Recognition Rate Explicit Knowledge Execution Plan Learn Vector Quantization Handwriting 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 2002

Authors and Affiliations

  • Miguel L. Bote-Lorenzo
    • 1
  • Yannis A. Dimitriadis
    • 1
  • Eduardo Gómez-Sánchez
    • 1
  1. 1.School of Telecommunications EngineeringUniversity of ValladolidValladolidSpain

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