Oracle text positioning system based on improved YOLOv7-FC model

Abstract

In recent years, how to use computer to efficiently and accurately recognize oracle characters on oracle rubbings has become the focus of research. Due to the severe damage caused by the natural environment and the characteristics of complex background and loud noise, the oracle bone rubbings are difficult to separate the foreground and back of the oracle bone inscriptions. Therefore, it will be the future research direction to eliminate the negative impact of complex background and accurately locate the Oracle bone inscriptions. This paper puts forward the YOLOv7-FC neural network model and realizes the specific application in the oracle topology data set, and presents the system of single oracle character positioning results.The experimental results show that on the HWOBC-A dataset, the mAP value of the YOLOv7-FC neural network model proposed in this paper reaches 0.937. Compared with the original YOLOv7 model, it has improved by 0.22, effectively balancing the relationship between running speed and average accuracy, and providing feasibility for Oracle text localization machine learning method.