  Scene text recognition (STR) has been an active research topic in computer
vision for years. To tackle this challenging problem, numerous innovative
methods have been successively proposed and incorporating linguistic knowledge
into STR models has recently become a prominent trend. In this work, we first
draw inspiration from the recent progress in Vision Transformer (ViT) to
construct a conceptually simple yet powerful vision STR model, which is built
upon ViT and outperforms previous state-of-the-art models for scene text
recognition, including both pure vision models and language-augmented methods.
To integrate linguistic knowledge, we further propose a Multi-Granularity
Prediction strategy to inject information from the language modality into the
model in an implicit way, i.e. , subword representations (BPE and WordPiece)
widely-used in NLP are introduced into the output space, in addition to the
conventional character level representation, while no independent language
model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push
the performance envelop of STR to an even higher level. Specifically, it
achieves an average recognition accuracy of 93.35% on standard benchmarks. Code
is available at
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR.
