Towards End-to-End In-Image Neural Machine Translation
arXiv:2010.10648
Abstract
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.
Accepted as an oral presentation at EMNLP, NLP Beyond Text workshop, 2020