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

Understand

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.