  The performance of a machine learning model degrades when it is applied to
data from a similar but different domain than the data it has initially been
trained on. The goal of domain adaptation (DA) is to mitigate this domain shift
problem by searching for an optimal feature transformation to learn a
domain-invariant representation. Such a domain shift can appear in handwriting
recognition (HWR) applications where the motion pattern of the hand and with
that the motion pattern of the pen is different for writing on paper and on
tablet. This becomes visible in the sensor data for online handwriting (OnHW)
from pens with integrated inertial measurement units. This paper proposes a
supervised DA approach to enhance learning for OnHW recognition between tablet
and paper data. Our method exploits loss functions such as maximum mean
discrepancy and correlation alignment to learn a domain-invariant feature
representation (i.e., similar covariances between tablet and paper features).
We use a triplet loss that takes negative samples of the auxiliary domain
(i.e., paper samples) to increase the amount of samples of the tablet dataset.
We conduct an evaluation on novel sequence-based OnHW datasets (i.e., words)
and show an improvement on the paper domain with an early fusion strategy by
using pairwise learning.
