Automatic handedness detection from off-line handwriting

Abstract

In forensics, the handedness detection or the classification of writers into left or right-handed helps investigators focusing more on a certain category of suspects. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. In this study, we propose a system which extract characterizing features from handwritings and use those features to perform the classification of handwritings with regards to handedness. Classification rates are reported on the QUWI dataset, reaching almost 70% for Left and right Handwriting. © 2013 IEEE.