Scene text recognition (STR) is a challenging task that requires large-scale
annotated data for training. However, collecting and labeling real text images
is expensive and time-consuming, which limits the availability of real data.
Therefore, most existing STR methods resort to synthetic data, which may
introduce domain discrepancy and degrade the performance of STR models. To
alleviate this problem, recent semi-supervised STR methods exploit unlabeled
real data by enforcing character-level consistency regularization between
weakly and strongly augmented views of the same image. However, these methods
neglect word-level consistency, which is crucial for sequence recognition
tasks. This paper proposes a novel semi-supervised learning method for STR that
incorporates word-level consistency regularization from both visual and
semantic aspects. Specifically, we devise a shortest path alignment module to
align the sequential visual features of different views and minimize their
distance. Moreover, we adopt a reinforcement learning framework to optimize the
semantic similarity of the predicted strings in the embedding space. We conduct
extensive experiments on several standard and challenging STR benchmarks and
demonstrate the superiority of our proposed method over existing
semi-supervised STR methods.

本文提出了一种新的半监督学习方法来进行场景文本识别（STR），该方法从视觉和语义两个方面融合了单词级一致性正则化，通过使用最短路径对齐模块以及采用强化学习框架来优化嵌入空间中预测字符串的语义相似性，实验结果表明该方法在多个标准和具有挑战性的 STR 基准测试中优于现有的半监督 STR 方法。