  Scene text recognition (STR) on Latin datasets has been extensively studied
in recent years, and state-of-the-art (SOTA) models often reach high accuracy.
However, the performance on non-Latin transcripts, such as Chinese, is not
satisfactory. In this paper, we collect six open-source Chinese STR datasets
and evaluate a series of classic methods performing well on Latin datasets,
finding a significant performance drop. To improve the performance on Chinese
datasets, we propose a novel radical-embedding (RE) representation to utilize
the ideographic descriptions of Chinese characters. The ideographic
descriptions of Chinese characters are firstly converted to bags of radicals
and then fused with learnable character embeddings by a
character-vector-fusion-module (CVFM). In addition, we utilize a bag of
radicals as supervision signals for multi-task training to improve the
ideographic structure perception of our model. Experiments show performance of
the model with RE + CVFM + multi-task training is superior compared with the
baseline on six Chinese STR datasets. In addition, we utilize a bag of radicals
as supervision signals for multi-task training to improve the ideographic
structure perception of our model. Experiments show performance of the model
with RE + CVFM + multi-task training is superior compared with the baseline on
six Chinese STR datasets.
