  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.
