  In this project, we leverage a trained single-letter classifier to predict
the written word from a continuously written word sequence, by designing a word
reconstruction pipeline consisting of a dynamic-programming algorithm and an
auto-correction model. We conduct experiments to optimize models in this
pipeline, then employ domain adaptation to explore using this pipeline on
unseen data distributions.
