  We introduce Inkorrect, a data- and label-efficient approach for online
handwriting (Digital Ink) spelling correction - DISC. Unlike previous work, the
proposed method does not require multiple samples from the same writer, or
access to character level segmentation. We show that existing automatic
evaluation metrics do not fully capture and are not correlated with the human
perception of the quality of the spelling correction, and propose new ones that
correlate with human perception. We additionally surface an interesting
phenomenon: a trade-off between the similarity and recognizability of the
spell-corrected inks. We further create a family of models corresponding to
different points on the Pareto frontier between those two axes. We show that
Inkorrect's Pareto frontier dominates the points that correspond to prior work.
