Abstract:
Handwriting and analysis of handwriting has been an active and interesting
area of research for many decades. Despite the growth of digital
media, handwriting retains its importance as being one of the very fine
motor skills that contributes to the intellectual development of individuals
as well. Computerized analysis of handwriting finds applications in
areas like handwriting recognition, signature verification, classification of
ancient manuscripts, forensic applications and authentication of writer of
a document. Anohter interesting aspect of handwriting is the existence of
correlation between writing and the different demographic attributes of the
writer. Although such studies have been carried out in psychological sciences
for many decades now, automation of this analysis through computer
programs is a relatively recent development. The system developed in our
study predicts the demographic attributes of an individual through offline
images of handwriting. The focus of our study lies on two such attributes,
gender and handedness. The methodology is based on extracting a set of
textural features from writing samples of different writers and training a
support vector machine classifier to learn the different demographic classes.
During evaluation phase, the features of the query writing sample are fed
to the trained classifier which outputs the class labels. The system trained
and evaluated on a benchmark database of handwritten samples (QUWI)
realized promising results.
