  Recently, vision-language joint representation learning has proven to be
highly effective in various scenarios. In this paper, we specifically adapt
vision-language joint learning for scene text detection, a task that
intrinsically involves cross-modal interaction between the two modalities:
vision and language, since text is the written form of language. Concretely, we
propose to learn contextualized, joint representations through vision-language
pre-training, for the sake of enhancing the performance of scene text
detectors. Towards this end, we devise a pre-training architecture with an
image encoder, a text encoder and a cross-modal encoder, as well as three
pretext tasks: image-text contrastive learning (ITC), masked language modeling
(MLM) and word-in-image prediction (WIP). The pre-trained model is able to
produce more informative representations with richer semantics, which could
readily benefit existing scene text detectors (such as EAST and PSENet) in the
down-stream text detection task. Extensive experiments on standard benchmarks
demonstrate that the proposed paradigm can significantly improve the
performance of various representative text detectors, outperforming previous
pre-training approaches. The code and pre-trained models will be publicly
released.
