Spontaneous Handwriting Text Recognition and Classification Using Finite-State Models

  • Alejandro Héctor Toselli
  • Moisés Pastor
  • Alfons Juan
  • Enrique Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3523)


Finite-state models are used to implement a handwritten text recognition and classification system for a real application entailing casual, spontaneous writing with large vocabulary. Handwritten short phrases which involve a wide variety of writing styles and contain many non-textual artifacts, are to be classified into a small number of predefined classes. To this end, two different types of statistical framework for phrase recognition-classification are considered, based on finite-state models. HMMs are used for text recognition process. Depending to the considered architecture, N-grams are used for performing text recognition and then text classification (serial approach) or for performing both simultaneously (integrated approach). The multinomial text classifier is also employed in the classification phase of the serial approach. Experimental results are reported which, given the extreme difficulty of the task, are encouraging.


Blank Space Word Error Rate Handwriting Recognition Recognition Phase Serial Approach 
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

  • Alejandro Héctor Toselli
    • 1
  • Moisés Pastor
    • 1
  • Alfons Juan
    • 1
  • Enrique Vidal
    • 1
  1. 1.Instituto Tecnológico de Informática and, Departamento de Sistemas Informáticos y Computación, ITI/DSICUniversidad Politécnica de ValenciaValenciaSpain

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