  Scene text recognition (STR) enables computers to recognize and read the text
in various real-world scenes. Recent STR models benefit from taking linguistic
information in addition to visual cues into consideration. We propose a novel
Masked Vision-Language Transformers (MVLT) to capture both the explicit and the
implicit linguistic information. Our encoder is a Vision Transformer, and our
decoder is a multi-modal Transformer. MVLT is trained in two stages: in the
first stage, we design a STR-tailored pretraining method based on a masking
strategy; in the second stage, we fine-tune our model and adopt an iterative
correction method to improve the performance. MVLT attains superior results
compared to state-of-the-art STR models on several benchmarks. Our code and
model are available at https://github.com/onealwj/MVLT.
