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.

翻译：在这个项目中,我们利用一个训练有素的单字母分类师,通过设计一个由动态-方案编制算法和自动校正模型组成的单词重建管道,从一个连续写成的单词序列中预测书面词。 我们进行了实验,以优化这一管道中的模型,然后利用域改造来探索如何利用这条管道来传播不可见的数据。