Relational Contrastive Learning for Scene Text Recognition
Jinglei Zhang Tiancheng Lin Yi Xu Kai Chen Rui Zhang
Context-aware methods achieved great success in supervised scene text
recognition via incorporating semantic priors from words. We argue that such
prior contextual information can be interpreted as the relations of textual
primitives due to the heterogeneous text and background, which can provide
effective self-supervised labels for representation learning. However, textual
relations are restricted to the finite size of dataset due to lexical
dependencies, which causes the problem of over-fitting and compromises
representation robustness. To this end, we propose to enrich the textual
relations via rearrangement, hierarchy and interaction, and design a unified
framework called RCLSTR: Relational Contrastive Learning for Scene Text
Recognition. Based on causality, we theoretically explain that three modules
suppress the bias caused by the contextual prior and thus guarantee
representation robustness. Experiments on representation quality show that our
method outperforms state-of-the-art self-supervised STR methods. Code is
available at https://github.com/ThunderVVV/RCLSTR.