  Recently self-supervised representation learning has drawn considerable
attention from the scene text recognition community. Different from previous
studies using contrastive learning, we tackle the issue from an alternative
perspective, i.e., by formulating the representation learning scheme in a
generative manner. Typically, the neighboring image patches among one text line
tend to have similar styles, including the strokes, textures, colors, etc.
Motivated by this common sense, we augment one image patch and use its
neighboring patch as guidance to recover itself. Specifically, we propose a
Similarity-Aware Normalization (SimAN) module to identify the different
patterns and align the corresponding styles from the guiding patch. In this
way, the network gains representation capability for distinguishing complex
patterns such as messy strokes and cluttered backgrounds. Experiments show that
the proposed SimAN significantly improves the representation quality and
achieves promising performance. Moreover, we surprisingly find that our
self-supervised generative network has impressive potential for data synthesis,
text image editing, and font interpolation, which suggests that the proposed
SimAN has a wide range of practical applications.
