Abstract: Aiming at the problem of poor recognition ability of existing methods for fuzzy and mutilated 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, assisting 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 module is proposed to better select and focus on information from specific channels. A mask attention module is also designed to improve the model's perception capabilities. Finally, a feature enhancement module 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 enhancing the recognition effect of fuzzy and mutilated Tangut characters. 

Key words: Tangut character recognition, multi-scale feature fusion, mask learning, inverse residual block