  Machine translation models have discrete vocabularies and commonly use
subword segmentation techniques to achieve an 'open vocabulary.' This approach
relies on consistent and correct underlying unicode sequences, and makes models
susceptible to degradation from common types of noise and variation. Motivated
by the robustness of human language processing, we propose the use of visual
text representations, which dispense with a finite set of text embeddings in
favor of continuous vocabularies created by processing visually rendered text
with sliding windows. We show that models using visual text representations
approach or match performance of traditional text models on small and larger
datasets. More importantly, models with visual embeddings demonstrate
significant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a
character permuted German-English task where subword models degrade to 1.9.
