Modal Contrastive Learning Based End-to-End Text Image Machine Translation

Text image machine translation (TIMT) aims at directly translating text in the source language embedded in images into the target language. Most existing systems follow the cascaded pipeline diagram from recognition to translation, which suffers from the problem of error propagation, parameter redundancy, and information reduction. The end-to-end model has the potential to alleviate these issues via bridging the recognition and translation models. However, the challenge is the data limitation and modality gap between text and image. In this paper, we propose a novel end-to-end model, namely Modal contrastive learning based End-to-end Text Image Machine Translation (METIMT), which alleviates these issues through end-to-end text image machine translation architecture and modal contrastive learning. Specifically, an image encoder is designed to encode images into the same feature space of corresponding text sentences, with the guidance of an intra-modal and inter-modal contrastive learning module. To further promote the research of text image machine translation, we have constructed one synthetic and two real-world datasets. Extensive experiments show that our lighter, faster model outperforms not only existing pipeline methods but also state-of-the-art end-to-end models on both synthetic and real-world evaluation sets. Our code and dataset will be released to the public.