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.

翻译：视觉模型因其在场景文本识别（STR）任务中的简洁性和高效性而日益受到关注。然而，由于缺乏语言知识和信息的感知能力，当前视觉模型面临两个问题：（1）纯视觉驱动的查询会导致注意力漂移，通常造成识别质量低下，本文将其归纳为语言不敏感漂移（LID）问题；（2）在某些视觉信息缺失的情况下（如遮挡等），视觉特征对识别而言并非最优。为解决这些问题，我们提出语言感知视觉模型（LPV），该模型挖掘视觉模型的语言能力以实现精确文本识别。为缓解LID问题，我们引入级联位置注意力（CPA）机制，通过逐步优化和语言信息挖掘获取高质量且精确的注意力图。此外，提出全局语言重构模块（GLRM），通过在视觉空间中感知语言信息来提升视觉特征的表示能力，在级联过程中将视觉特征逐步转化为语义丰富的特征。与以往方法不同，本方法在保持低复杂度的同时取得了最优结果（仅8.11M参数即可达到92.4%的准确率）。代码开源地址：https://github.com/CyrilSterling/LPV。