  Linguistic knowledge has brought great benefits to scene text recognition by
providing semantics to refine character sequences. However, since linguistic
knowledge has been applied individually on the output sequence, previous
methods have not fully utilized the semantics to understand visual clues for
text recognition. This paper introduces a novel method, called Multi-modAl Text
Recognition Network (MATRN), that enables interactions between visual and
semantic features for better recognition performances. Specifically, MATRN
identifies visual and semantic feature pairs and encodes spatial information
into semantic features. Based on the spatial encoding, visual and semantic
features are enhanced by referring to related features in the other modality.
Furthermore, MATRN stimulates combining semantic features into visual features
by hiding visual clues related to the character in the training phase. Our
experiments demonstrate that MATRN achieves state-of-the-art performances on
seven benchmarks with large margins, while naive combinations of two modalities
show less-effective improvements. Further ablative studies prove the
effectiveness of our proposed components. Our implementation is available at
https://github.com/wp03052/MATRN.
