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The performance of a machine learning model degrades when it is applied todata from a similar but different domain than the data it has initially beentrained on. The goal of domain adaptation (DA) is to mitigate this domain shiftproblem by searching for an optimal feature transformation to learn adomain-invariant representation. Such a domain shift can appear in handwritingrecognition (HWR) applications where the motion pattern of the hand and withthat the motion pattern of the pen is different for writing on paper and ontablet. This becomes visible in the sensor data for online handwriting (OnHW)from pens with integrated inertial measurement units. This paper proposes asupervised DA approach to enhance learning for OnHW recognition between tabletand paper data. Our method exploits loss functions such as maximum meandiscrepancy and correlation alignment to learn a domain-invariant featurerepresentation (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 byusing pairwise learning.

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition