Sampling and Ranking for Digital Ink Generation on a tight computational
  budget
Andrei Afonin Andrii Maksai Aleksandr Timofeev Claudiu Musat
Digital ink (online handwriting) generation has a number of potential
applications for creating user-visible content, such as handwriting
autocompletion, spelling correction, and beautification. Writing is personal
and usually the processing is done on-device. Ink generative models thus need
to produce high quality content quickly, in a resource constrained environment.
  In this work, we study ways to maximize the quality of the output of a
trained digital ink generative model, while staying within an inference time
budget. We use and compare the effect of multiple sampling and ranking
techniques, in the first ablation study of its kind in the digital ink domain.
  We confirm our findings on multiple datasets - writing in English and
Vietnamese, as well as mathematical formulas - using two model types and two
common ink data representations. In all combinations, we report a meaningful
improvement in the recognizability of the synthetic inks, in some cases more
than halving the character error rate metric, and describe a way to select the
optimal combination of sampling and ranking techniques for any given
computational budget.