Simplified Chinese to Traditional Chinese script conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a novel model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character Conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to preprocess text for topic classification. An error analysis reveals that our method’s particular strengths are in dealing with code mixing and named entities.