Scene Text Recognition (STR) is a challenging task due to variations in text
style, shape, and background. Incorporating linguistic information is an
effective way to enhance the robustness of STR models. Existing methods rely on
permuted language modeling (PLM) or masked language modeling (MLM) to learn
contextual information implicitly, either through an ensemble of permuted
autoregressive (AR) LMs training or iterative non-autoregressive (NAR) decoding
procedure. However, these methods exhibit limitations: PLM's AR decoding
results in the lack of information about future characters, while MLM provides
global information of the entire text but neglects dependencies among each
predicted character. In this paper, we propose a Masked and Permuted Implicit
Context Learning Network for STR, which unifies PLM and MLM within a single
decoding architecture, inheriting the advantages of both approaches. We utilize
the training procedure of PLM, and to integrate MLM, we incorporate word length
information into the decoding process by introducing specific numbers of mask
tokens. Experimental results demonstrate that our proposed model achieves
state-of-the-art performance on standard benchmarks using both AR and NAR
decoding procedures.

本文提出了一种利用语言信息来增强场景文本识别模型鲁棒性的方法，通过将 Masked Language Modeling (MLM) 和 Permuted Language Modeling (PLM) 结合到一个单一解码体系结构中，引入特定数量的掩码标记来集成 MLM，实验结果表明，该模型在标准基准测试中使用 AR 和 NAR 解码程序均获得了最先进的性能。