【摘要】 针对满文字符识别领域中非标准形态变体和一音多形等固有挑战，提出了一种基于Swin Transformer架构的多尺度特征融合模型(Multi-scale feature fusion based Swin Transformer, MR-SwinT).该模型通过引入多分辨率并行输入机制，实现了字符的细粒度局部特征与宏观语境信息的协同捕获.模型的核心优势在于充分利用了Swin Transformer的层级式窗口自注意力机制，该机制为大尺度特征建模提供了卓越的表达能力.此外，本文设计的SMTBlocks模块通过自适应加权调整策略，能有效实现多分辨率特征的动态融合，显著增强了模型对复杂字符的区分能力与泛化性能.实验结果表明MR-SwinT模型整词识别准确率为96.59%,单字符识别准确率为99.46%.更多还原

【Abstract】 To address the inherent challenges of non-standard morphological variants and multiple graphemic representations of the same phoneme in Manchu character recognition, this paper proposes MR-SwinT,a multi-scale feature fusion model based on the Swin Transformer architecture.The model enables synchronized capture of fine-grained local character features and macro-contextual information via a multi-resolution parallel input mechanism.A core advantage of the model is its full leverage of the Swin Transformer hierarchical, window-based self-attention mechanism, which offers exceptional representational capacity for large-scale feature modeling.Additionally, the SMT Blocks module, specifically designed in this study, achieves effective dynamic fusion of multi-resolution features through an adaptive weighting adjustment strategy, significantly enhancing the model discriminative power and generalization ability for complex characters.Experimental results indicate that the MR-SwinT model attains 96.59% accuracy for whole-word recognition and 99.46% accuracy for single-character recognition.更多还原