Ancient Chinese Character Image Retrieval Based on Self-Attention Mechanism and Multi-Scale Feature Fusion

The diverse fonts and forms of ancient Chinese characters and the significant differences in glyphs present great challenges for retrieving ancient Chinese character images. In this study, we proposed a Chinese character image retrieval model based on a self-attention mechanism and multi-scale feature fusion SAMSFF to improve the feature extraction ability and accuracy of Chinese character images in ancient documents. Firstly, an improved inverted residual module called HardFused IB was constructed using the optimized SE attention mechanism to obtain the enhanced features of key information. Secondly, the static dynamic context fusion module was used to fully use the context information between adjacent keys to improve the expressiveness and representativeness of the output features. Finally, the bilinear multi-scale feature fusion module BMSFblock was constructed to perform an adaptive fusion of the multi-layer features extracted by the designed network. The network measures the Euclidean distance between the queried and candidate images and sorts and returns the most relevant results. The mAP@-1 of the retrieval method proposed in this paper on the ancient Chinese character image dataset is 0.932. Experimental results show that the model can effectively extract the features of ancient Chinese character images, improve retrieval accuracy, and have certain advantages in ancient Chinese character image retrieval.