A Set of Handwriting Features for Use in Automated Writer Identification^,

TLDR

Writer identity can be characterized by physical feature distributions, and a graph‑based system quantifies these features, though the process is computationally intensive and relies on statistical pattern recognition. The article aims to quantify physical handwriting features and develop pattern recognition methods to discriminate writers. The method segments handwriting into graphemes, skeletonizes them to obtain graph topology, then matches graphemes by topology and geometry, comparing graphs from known writers to those from unknown writings to build pattern recognition models. The system achieves high accuracy and is largely language‑independent in recognizing cursive writers.

Abstract

Abstract A writer's biometric identity can be characterized through the distribution of physical feature measurements (“writer's profile”); a graph‐based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms (“graphemes”), which are “skeletonized” to yield the graphical topology of the handwritten segment. The graph‐based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph‐based system described in this article has been implemented in a highly accurate and approximately language‐independent biometric recognition system of writers of cursive documents.