Functional Components of Variation in Handwriting

Abstract

Functional data analysis techniques are used to analyze a sample of handwriting in Chinese. The goals are (a) to identify a differential equation that satisfactorily models the data's dynamics, and (b) to use the model to classify handwriting samples taken from differential individuals. After preliminary smoothing and registration steps, a second-order linear differential equation, for which the forcing function is small, is found to provide a good reconstruction of the original script records. The equation is also able to capture a substantial amount of the variation in the scripts across replication. The cross-validated classification process is 100% effective for the samples analyzed.

Key Words:

• Classification
• Differential equations
• Dynamic model
• Functional data analysis
• Longitudinal data
• Penalized nonparametric regression
• Principal differential analysis
• Registration
• Smoothing spline
• Time warping.