Multi-attention ghost pyramid fusion network for script identification of Chinese ancient document images

Script identification is a key step in document analysis and recognition in multilingual environments. This study proposed a new dataset for script identification algorithms, containing images of ancient documents in 12 different ethnic scripts, including Chinese script, Naxi Dongba script, Yi script, Shui script, Tangut script, ancient Zhuang script, ancient Buyi script, Tibetan script, Dai script, Chagatai script, Mongolian script, and Manchu script. Focusing on the high accuracy required for ancient script identification, this study proposed an method named multi-attention ghost pyramid fusion network (MAGPNet). MAGPNet consists of a feature extraction network, a channel feature pyramid, and a Multi-Headed Self-Attention Bottleneck Block. The feature extraction network utilizes lightweight convolutional modules and parameter-free attention modules to enhance MAGPNet's feature extraction capability while maintaining a lighter structure. The channel feature pyramid increases the model's robustness in processing ancient documents of different scales. The Multi-Headed Self-Attention Bottleneck Block, by introducing a Multi-Headed Self-Attention, focuses on effective features. Experiments demonstrate that MAGPNet achieves a 99.97% accuracy rate on the multilingual ancient document image script identification dataset, maintaining excellent classification performance across multiple datasets.