Component-Level Segmentation for Oracle Bone Inscription Decipherment

Oracle Bone Inscriptions (OBIs), as the earliest systematically organized pictographic script in China, hold significant importance in the study of the origins of Chinese civilization. Of the approximately 4,500 excavated OBI characters, only about one-third have been deciphered, leaving the remaining characters shrouded in mystery. Over the past decade, an increasing number of researchers have attempted to leverage artificial intelligence to assist in deciphering OBIs, but these efforts have not yet fully met the demands of this challenging objective. In this paper, we identify a key task—Component-Level OBI Segmentation—based on a successful deciphering case from 2018. This task aims to help experts quickly identify specific components within OBIs, thereby accelerating the deciphering process. Accordingly, we propose a new model to accomplish this task. Our model leverages a small amount of annotated data and a large amount of weakly annotated data and incorporates expert-provided prior knowledge, i.e., stroke rules, to automatically segment OBI components. Additionally, we train a series of auxiliary classifiers to evaluate the segmentation results during the test stage. We also invite experts to conduct a professional assessment of the results, which we cross-validated against our proposed evaluation metrics. Experimental results demonstrate that our method can accurately and clearly present the segmented components to experts.