Detecting oracle bone inscriptions via pseudo-category labels

Abstract

Oracle bone inscriptions (OBIs) are the earliest Chinese characters and reserve abundant historical information. OBIs are detected by locating their positions in digital images. This has been a foundational task in modern archeological studies. Due to the development of deep neural networks(DNNs) in computer vision, detecting OBIs can be implemented by a more concise method instead of designing complicated hand-crafted features. However, existing models cannot perform well when noise areas are similar to some inscriptions. In this work, we present a simple but effective pseudo-label-based architecture for OBIs detection. Different from previous approaches, our method performs OBIs detection with the employment of information from multilabel annotations rather than single location information. We append a plug-and-play module that predicts the pseudo-label of an inscription after the backbone network for learning the particular structure prior to each inscription and brings this information to the backbone network by means of feature fusion. We make remarkable improvements on different backbone networks when using the proposed method on an OBIs detection dataset. The quantitative and qualitative results show that the proposed model can detect OBIs well and is an effective tool for assisting in the discovery and recognition of ancient writing.