Perceptual features for off-line handwritten word recognition: A framework for heuristic prediction, representation and matching

  • Sriganesh Madhvanath
  • Venu Govindaraju
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

Perceptual holistic features are visually conspicuous features of the word shape that have been cited in reading studies as being utilized in fluent reading. While these features have been used for word recognition when the lexicon of possible words is small and static, their application to the general problem of omni-scriptor handwritten word recognition is limited by their variability at the word level and the paucity of samples for word-level training. A methodology of coarse holistic features and heuristic prediction of ideal features from ASCII is proposed to address these issues. The methodology is based on the view that real world examples of handwritten words are instances of the ideal exemplar of the word class distorted by the scriptor, stylus, medium and intervening electronic imaging processes, and has applications in verification and lexicon reduction for handwritten word recognition.

Keywords

holistic approach handwritten word recognition perceptual features graph-matching handwriting models syntactic approach 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Sriganesh Madhvanath
    • 1
  • Venu Govindaraju
    • 2
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.Center of Excellence for Document Analysis and Recognition (CEDAR) Department of Computer ScienceState University of New York at BuffaloBuffaloUSA

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