Sampling and Ranking for Digital Ink Generation on a tight computational budget
This work addresses the need for efficient on-device digital ink generation for applications like handwriting autocompletion, though it is incremental as it focuses on optimizing existing methods.
The study tackled the problem of generating high-quality digital ink within a tight computational budget by comparing sampling and ranking techniques, resulting in meaningful improvements such as more than halving the character error rate in some cases.
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.