Writer Identification: Statistical Analysis and Dichotomizer

  • Sung-Hyuk Cha
  • Sargur N. Srihari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper is to determine the statistical validity of individuality in handwriting based on measurement of features, quantification and statistical analysis. In classification problems such as writer, face, finger print or speaker identification, the number of classes is very large or unspecified. To establish the inherent distinctness of the classes, i.e., validate individuality, we transform the many class problem into a dichotomy by using a “distance” between two samples of the same class and those of two different classes. A measure of confidence is associated with individuality. Using ten feature distance values, we trained an artificial neural network and obtained 97% overall correctness. In this experiment, 1,000 people provided three sample handwritings.

Key Words

Dichotomizer Hypothesis Testing Individuality Writer Identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Sung-Hyuk Cha
    • 1
  • Sargur N. Srihari
    • 1
  1. 1.Center of Excellence for Document Analysis and RecognitionState University of New York at Buffalo

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