Abstract
Oracle bone inscriptions, as the earliest writing system in China, are of great cultural and academic value. In recent years, deep learning method has been widely used in the process of oracle bone character examination and interpretation, and has achieved certain effect in oracle bone character recognition. However, little progress has been made in the unknown oracle bone character recognition. To solve these problems, this paper collects four types of characters: oracle bone script, jinwen, Seal script and official script as the data set, and uses the similarity between the characters and the depth network model to build the oracle bone character detection and recognition model. Then, this paper improved the Tiny-YOLOv4 model to realize the function of unknown oracular character detection and enhanced the detection speed by changing the NMS (non-maximum Suppression). The experiment proved that the detection and recognition accuracy of oracle bone characters reached 67.28%, and the detection speed of 2.281ms was improved by using NMS strategy.
Published Version
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