Damaged Inscription Recognition Based on Hierarchical Decomposition Embedding and Bipartite Graph
Ancient inscriptions carry rich historical and cultural information. However, due to natural weathering and man-made destruction, the text information on the inscriptions is incomplete. The semantic information of ancient inscriptions is diverse and the text examples of ancient inscription are insufficient, which make it very difficult to learn the semantic information between Chinese characters for recognizing damaged characters. The challenging task of damaged characters recognition and understanding by Chinese character spatial semantic modeling is attempted to be solved in this paper. Based on Hierarchical Decomposition Embedding(HDE), the proposed DynamicGrape performs feature mapping on damaged character image and determines whether it is damaged. If character is not damaged, its image is directly converted into hierarchical decomposition embedding to reason the edge weight of the bipartite graph for recognizing Chinese character. If character is damaged, it is necessary to search for possible Chinese characters and components in the encoding set, select the feature dimension of HDE from image mapping, and input the bipartite graph to infer the possible Chinese character. In the self-built dataset and Chinese Text in the Wild(CTW) dataset, the experimental results show that the bipartite graph network can not only transfer and infer Chinese character pattern of damaged characters effectively, but also precisely recognize and understand damaged Chinese characters. It opens up new ideas for the damaged structure information processing.
Damaged inscriptionInscription predictionInscription recognitionDamaged text recognitionBipartite graph neural networks