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Scene text recognition (STR) on Latin datasets has been extensively studiedin recent years, and state-of-the-art (SOTA) models often reach high accuracy.However, the performance on non-Latin transcripts, such as Chinese, is notsatisfactory. In this paper, we collect six open-source Chinese STR datasetsand evaluate a series of classic methods performing well on Latin datasets,finding a significant performance drop. To improve the performance on Chinesedatasets, we propose a novel radical-embedding (RE) representation to utilizethe ideographic descriptions of Chinese characters. The ideographicdescriptions of Chinese characters are firstly converted to bags of radicalsand then fused with learnable character embeddings by acharacter-vector-fusion-module (CVFM). In addition, we utilize a bag ofradicals as supervision signals for multi-task training to improve theideographic structure perception of our model. Experiments show performance ofthe model with RE + CVFM + multi-task training is superior compared with thebaseline on six Chinese STR datasets. In addition, we utilize a bag of radicalsas supervision signals for multi-task training to improve the ideographicstructure perception of our model. Experiments show performance of the modelwith RE + CVFM + multi-task training is superior compared with the baseline onsix Chinese STR datasets.

Reading Chinese in Natural Scenes with a Bag-of-Radicals Prior