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Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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Automatic writer identification using connected-component contours and edge-based features of uppercase Western

Lambert Schomaker1, Marius Bulacu

  • 1AI Institute, Groningen University, Grote Kuisstraat 2/1, Groningen, The Netherlands. schomaker@ai.rug.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 27, 2008
PubMed
Summary

This study introduces a novel offline writer identification method using connected-component contours (COCOCOs or CO3s) for uppercase handwriting. The technique accurately identifies writers from a single sentence, bridging image statistics and character feature analysis.

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Pattern Recognition

Background:

  • Writer identification is crucial in forensic document examination.
  • Existing methods often rely on manual feature extraction or general image statistics.

Purpose of the Study:

  • To develop a novel, automated offline writer identification technique.
  • To utilize connected-component contours (COCOCOs or CO3s) for writer characterization.

Main Methods:

  • A stochastic pattern generator model was employed to characterize writers.
  • A codebook of CO3s was created from a training set.
  • Probability-density functions (PDFs) of CO3s were computed for an independent test set.

Main Results:

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  • The CO3 PDF demonstrated high sensitivity in identifying individual writers from uppercase text.
  • The approach successfully bridges the gap between image-statistics and manual feature extraction.
  • Combining CO3 PDF with edge-based orientation and curvature PDFs yielded very high identification rates.

    • Conclusions:

    • The proposed COCOCO-based method offers an effective and automated solution for offline writer identification.
    • This technique shows promise for applications requiring high accuracy in distinguishing handwriting samples.
    • The integration of contour-based features enhances the robustness of writer identification systems.