Invariant features for HMM based on-line handwriting recognition

  • Jianying Hu
  • Michael K. Brown
  • William Turin
Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 974)


In this paper we address the problem variability in handwriting due to geometric distortion of letters and words by rotation, scale and translation. In general, translation has not been a problem because it is easy to choose features that are invariant with respect to translation. It is more difficult to find features that are invariant with respect to all three types of geometric distortion. We introduce two new features for HMM based handwriting recognition that are invariant with respect to translation, rotation and scale changes. These are termed ratio of tangents and normalized curvature. Writer-independent recognition error in our system is reduced by a factor of over 50% by employing these features.


Invariant Feature Geometric Distortion Handwriting Recognition Tangent Slope Smooth Planar Curve 
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 1995

Authors and Affiliations

  • Jianying Hu
    • 1
  • Michael K. Brown
    • 1
  • William Turin
    • 1
  1. 1.AT&T Bell LaboratoriesMurray HillUSA

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