  Dominant scene text recognition models commonly contain two building blocks,
a visual model for feature extraction and a sequence model for text
transcription. This hybrid architecture, although accurate, is complex and less
efficient. In this study, we propose a Single Visual model for Scene Text
recognition within the patch-wise image tokenization framework, which dispenses
with the sequential modeling entirely. The method, termed SVTR, firstly
decomposes an image text into small patches named character components.
Afterward, hierarchical stages are recurrently carried out by component-level
mixing, merging and/or combining. Global and local mixing blocks are devised to
perceive the inter-character and intra-character patterns, leading to a
multi-grained character component perception. Thus, characters are recognized
by a simple linear prediction. Experimental results on both English and Chinese
scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L
(Large) achieves highly competitive accuracy in English and outperforms
existing methods by a large margin in Chinese, while running faster. In
addition, SVTR-T (Tiny) is an effective and much smaller model, which shows
appealing speed at inference. The code is publicly available at
https://github.com/PaddlePaddle/PaddleOCR.
