  End-to-end text image translation (TIT), which aims at translating the source
language embedded in images to the target language, has attracted intensive
attention in recent research. However, data sparsity limits the performance of
end-to-end text image translation. Multi-task learning is a non-trivial way to
alleviate this problem via exploring knowledge from complementary related
tasks. In this paper, we propose a novel text translation enhanced text image
translation, which trains the end-to-end model with text translation as an
auxiliary task. By sharing model parameters and multi-task training, our model
is able to take full advantage of easily-available large-scale text parallel
corpus. Extensive experimental results show our proposed method outperforms
existing end-to-end methods, and the joint multi-task learning with both text
translation and recognition tasks achieves better results, proving translation
and recognition auxiliary tasks are complementary.
