A multi-layer mask recognition method for Tangut characters
Aiming at the problem of poor recognition ability of existing methods for fuzzy and mutila-ted Tangut characters,a Tangut character recognition model MMSFTR is proposed.Firstly,a multi-layer mask learning strategy is introduced to extract key character features in a hierarchical manner,as-sisting the model in understanding the internal structure of the Tangut characters more efficiently,and improving its ability to describe complex features of Tangut characters.Secondly,a multi-scale feature fusion module is designed to extract richer multi-scale features.Then,a channel adaptive attention mod-ule is proposed to better select and focus on information from specific channels.A mask attention mod-ule is also designed to improve the model's perception capabilities.Finally,a feature enhancement mod-ule is designed to optimize multi-level features of the network and enhance deep-level features.Through the collaborative work of these 4 modules,MMSFTR achieves the desired results.Experimental results show that MMSFTR achieves a recognition accuracy of 99.40％on the TCD-E dataset,effectively en-hancing the recognition effect of fuzzy and mutilated Tangut characters.
Tangut character recognitionmulti-scale feature fusionmask learninginverse residual block
万方数据
维普
