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Dynamical encoding of cursive handwriting

Y Singer1, N Tishby

  • 1Institute of Computer Science, Hebrew University, Jerusalem, Israel.

Biological Cybernetics
|January 1, 1994
PubMed
Summary
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This study introduces a novel model for online cursive handwriting analysis. It represents handwriting as modulated cycloidal motion, enabling efficient encoding and recognition of scripts.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Online handwriting analysis traditionally faces challenges in accurately capturing dynamic writing features.
  • Existing models often struggle with the continuous and complex nature of cursive scripts.

Purpose of the Study:

  • To present and evaluate a model-based approach for online cursive handwriting analysis and recognition.
  • To develop a method for efficiently encoding and representing dynamic pen trajectories.

Main Methods:

  • Modeled online handwriting as a modulation of cycloidal pen motion with coupled oscillations and linear drift.
  • Developed a procedure for estimating and quantizing cycloidal motion parameters for arbitrary handwriting.
  • Utilized a discrete motor control representation derived from quantized model parameters.

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Main Results:

  • The proposed model efficiently encodes general pen trajectories by modulating amplitudes and phase lags.
  • Quantization of parameters retained writing intelligibility while enabling discrete representation.
  • The discrete motor control representation facilitated successful word spotting and matching of cursive scripts.

Conclusions:

  • The dynamic representation derived from modulated cycloidal motion shows significant potential for complete cursive handwriting recognition.
  • This model-based approach offers an effective method for analyzing and recognizing online cursive handwriting.
  • Further research can explore the application of this model in various handwriting recognition systems.