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Word Recognition by MLP-based Character Spotting and Dynamic Programming

  • F. Camastra
  • E. Cepollina
  • A. M. Colla
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

This paper describes a method for handprinted word recognition, with the following characteristics: traditional pre-processing (relevant to single characters, obtained by word segmentation) is replaced by pre-processing based on piecewise normalization applied at whole words; feature extraction and character classification by MLP are performed in a sliding window fashion; the output string is matched with an ASCII word vocabulary by Dynamic Programming with the Levenshtein distance; a list of word candidates is issued. Afterwards, when the language is formally known, an appropriate parser can be applied to full Sentence Recognition. Tests on a medium size vocabulary show extremely promising results.

Keywords

Word Recognition Neural Information Processing System Word Segmentation Word Image Levenshtein Distance 
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 London Limited 1998

Authors and Affiliations

  • F. Camastra
    • 1
  • E. Cepollina
    • 2
  • A. M. Colla
    • 1
  1. 1.Elsag Bailey - Un’Azienda Finmeccanica S.p.AGenovaItaly
  2. 2.Dip. Informatica e Scienze dell’InformazioneUniversità di GenovaGenovaItaly

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