Oracle Bone Inscriptions Extraction by Using Weakly Supervised Instance Segmentation under Deep Network

Abstract

Oracle-bone inscriptions (OBIs), the oldest hieroglyphs in China, were mainly carved on cattle scapulars and tortoise shells, as well as other animal bones. However, automatically extracting OBI characters is a rather complex task due to their differences in character size, orientation, alignment and noisy background. Conventional techniques like Laplacian operation, gradient-edge, or connected component, cannot obtain satisfying results. Therefore, in this paper, instance segmentation methods under deep convolutional neural network were exploited to extract OBIs automatically. More specifically, a SOTA weakly supervised instance segmentation model was introduced to solve this problem, considering that the pixel-level annotation is notoriously time-consuming compared to the bounding boxes annotation, which is extremely serious for the annotation of OBI images because annotators' lack of domain knowledge. The model was trained by 3228 oracle rubbing images and were tested on 312 ones. Results demonstrated that this method can provide a feasible way to automatically extract OBIs from rubbing images (as shown in Fig. 1).