  Despite the recent remarkable improvements in scene text recognition (STR),
the majority of the studies focused mainly on the English language, which only
includes few number of characters. However, STR models show a large performance
degradation on languages with a numerous number of characters (e.g., Chinese
and Korean), especially on characters that rarely appear due to the long-tailed
distribution of characters in such languages. To address such an issue, we
conducted an empirical analysis using synthetic datasets with different
character-level distributions (e.g., balanced and long-tailed distributions).
While increasing a substantial number of tail classes without considering the
context helps the model to correctly recognize characters individually,
training with such a synthetic dataset interferes the model with learning the
contextual information (i.e., relation among characters), which is also
important for predicting the whole word. Based on this motivation, we propose a
novel Context-Aware and Free Experts Network (CAFE-Net) using two experts: 1)
context-aware expert learns the contextual representation trained with a
long-tailed dataset composed of common words used in everyday life and 2)
context-free expert focuses on correctly predicting individual characters by
utilizing a dataset with a balanced number of characters. By training two
experts to focus on learning contextual and visual representations,
respectively, we propose a novel confidence ensemble method to compensate the
limitation of each expert. Through the experiments, we demonstrate that
CAFE-Net improves the STR performance on languages containing numerous number
of characters. Moreover, we show that CAFE-Net is easily applicable to various
STR models.
