Bronze inscription classification based on rare-class sample generator

Bronze Inscriptions is essential to the study of ancient Chinese history. We have obtained a set of materials containing 102,338 images of bronze texts collected by experts, but the distribution of these acquired images is imbalance, with a large sample of common words but the opposite for rare words. To solve this problem, we have adopted an end-to-end enhanced rare data generation module that can generate feature maps for rare classes during the training phase. The experiment results show that the proposed model can obtain an overall average accuracy of 77.38% and a minority accuracy of 68.24%.