The study of ancient Chinese bamboo slips from the Warring States period is a crucial task in digital humanities, yet it is hampered by severe background noise and long-tailed character distributions. To address these issues, we propose a framework centered on a Self-supervised Bilateral Branch Network (SBBN). The framework makes three key contributions: (1) We establish zgzj1001, a benchmark dataset for this task. (2) We design a Spatial Self-Attention module to specifically enhance character features while actively suppressing background interference. (3) SBBN effectively overcomes the class imbalance problem by integrating a self-supervised contrastive learning branch to learn robust representations of rare tail classes. Experiments on zgzj1001 show that our method achieves a 96.31% balanced accuracy, offering a competitive and practical solution compared to baseline models. This work provides a reliable tool for paleographic research and a functional paradigm for cultural heritage digitization.
Jiang et al. (Sat,) studied this question.