  In this paper, we offer a preliminary investigation into the task of in-image
machine translation: transforming an image containing text in one language into
an image containing the same text in another language. We propose an end-to-end
neural model for this task inspired by recent approaches to neural machine
translation, and demonstrate promising initial results based purely on
pixel-level supervision. We then offer a quantitative and qualitative
evaluation of our system outputs and discuss some common failure modes.
Finally, we conclude with directions for future work.
