Pixel-based Encoding of Language

Natural language processing plays an increasingly important part of our digital lives but it works best for high-resource languages due to the amount of data needed to train models. This project will create new models that can process any language rendered in an image, based on the insight that written languages share visual similarities, resulting in high-quality models for thousands of languages. This grant will fund two PhD students, one postdoc, and equipment.