  Modeling semantic information is helpful for scene text recognition. In this
work, we propose to model semantic and visual information jointly with a
Visual-Semantic Transformer (VST). The VST first explicitly extracts primary
semantic information from visual feature maps with a transformer module and a
primary visual-semantic alignment module. The semantic information is then
joined with the visual feature maps (viewed as a sequence) to form a pseudo
multi-domain sequence combining visual and semantic information, which is
subsequently fed into an transformer-based interaction module to enable
learning of interactions between visual and semantic features. In this way, the
visual features can be enhanced by the semantic information and vice versus.
The enhanced version of visual features are further decoded by a secondary
visual-semantic alignment module which shares weights with the primary one.
Finally, the decoded visual features and the enhanced semantic features are
jointly processed by the third transformer module obtaining the final text
prediction. Experiments on seven public benchmarks including regular/ irregular
text recognition datasets verifies the effectiveness our proposed model,
reaching state of the art on four of the seven benchmarks.
