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A clustering method for graphical handwriting components and statistical writership analysis.

Amy M Crawford1, Nicholas S Berry1,2, Alicia L Carriquiry1

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This study introduces a novel method for handwriting analysis, focusing on character shapes for forensic document examination. It enables accurate writer identification by clustering graphical structures and modeling individual writing propensities.

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Area of Science:

  • Computer Vision
  • Forensic Science
  • Pattern Recognition

Background:

  • Handwritten documents can be analyzed by content or character shape.
  • Forensic applications require comparing handwriting to documents of unknown origin.

Purpose of the Study:

  • To develop a method for comparing handwriting based on character shapes.
  • To create a system for identifying writers from scanned documents using structural attributes.

Main Methods:

  • Decomposing handwritten documents into small graphical structures (letters).
  • Introducing a graph edit distance-inspired measure for structure comparison.
  • Utilizing an outlier-tolerant K-means algorithm for clustering graphs.
  • Developing a Bayesian hierarchical model for writer propensity.

Main Results:

  • Successfully clustered graphical structures based on shape.
  • Demonstrated effectiveness in handwriting identification tasks.
  • Outperformed less flexible grouping methods in handling outliers.

Conclusions:

  • The proposed method provides an effective approach for handwriting analysis and writer identification.
  • The outlier-tolerant clustering and Bayesian modeling enhance accuracy in forensic document examination.