Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning
This work addresses the challenge of quantifying similarity for oracle bone script characters, which could aid in cracking unknown inscriptions, but it is incremental as it combines existing techniques.
The paper tackles the problem of screening similar characters in oracle bone script by proposing a method that integrates ResNet-50 self-supervised and RepVGG supervised learning using fuzzy comprehensive evaluation, outputting the most similar image without manual intervention.
This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResNet-50 self-supervised and RepVGG supervised learning. The source image dataset HWOBC oracle is taken as input, the target image is selected, and finally the most similar image is output in turn without any manual intervention. The same feature encoding method is not used for images of different modalities. Before the model training, the image data is preprocessed, and the image is enhanced by random rotation processing, self-square graph equalization theory algorithm, and gamma transform, which effectively enhances the key feature learning. Finally, the fuzzy comprehensive evaluation method is used to combine the results of supervised training and unsupervised training, which can better solve the "most similar" problem that is difficult to quantify. At present, there are many unknown oracle-bone inscriptions waiting for us to crack. Contacting with the glyphs can provide new ideas for cracking.