Using Automated Comparisons to Quantify Handwriting Individuality

TLDR: This paper provides a strategy for bounding the probability of observing two writers with indistinguishable writing profiles with a random match probability that can be estimated statistically and shows how this bound can be used when designing an empirical study of individuality.

Abstract: The proposition that writing profiles are unique is considered a key premise underlying forensic handwriting comparisons. An empirical study cannot validate this proposition because of the impossibility of observing sample documents written by every individual. The goal of this paper is to illustrate what can be stated about the individuality of writing profiles using a database of handwriting samples and an automated comparison procedure. In this paper, we provide a strategy for bounding the probability of observing two writers with indistinguishable writing profiles (regardless of the comparison methodology used) with a random match probability that can be estimated statistically. We illustrate computation of this bound using a convenience sample of documents and an automated comparison procedure based on Pearson's chi-squared statistic applied to frequency distributions of letter shapes extracted from handwriting samples. We also show how this bound can be used when designing an empirical study of individuality.