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.
@article{arxiv.2101.06025,
title = {Motion-Based Handwriting Recognition and Word Reconstruction},
author = {Junshen Kevin Chen and Wanze Xie and Yutong He},
journal= {arXiv preprint arXiv:2101.06025},
year = {2021}
}