  Vision model have gained increasing attention due to their simplicity and
efficiency in Scene Text Recognition (STR) task. However, due to lacking the
perception of linguistic knowledge and information, recent vision models suffer
from two problems: (1) the pure vision-based query results in attention drift,
which usually causes poor recognition and is summarized as linguistic
insensitive drift (LID) problem in this paper. (2) the visual feature is
suboptimal for the recognition in some vision-missing cases (e.g. occlusion,
etc.). To address these issues, we propose a $\textbf{L}$inguistic
$\textbf{P}$erception $\textbf{V}$ision model (LPV), which explores the
linguistic capability of vision model for accurate text recognition. To
alleviate the LID problem, we introduce a Cascade Position Attention (CPA)
mechanism that obtains high-quality and accurate attention maps through
step-wise optimization and linguistic information mining. Furthermore, a Global
Linguistic Reconstruction Module (GLRM) is proposed to improve the
representation of visual features by perceiving the linguistic information in
the visual space, which gradually converts visual features into semantically
rich ones during the cascade process. Different from previous methods, our
method obtains SOTA results while keeping low complexity (92.4% accuracy with
only 8.11M parameters). Code is available at
https://github.com/CyrilSterling/LPV.
