  Scene text recognition (STR) has been widely studied in academia and
industry. Training a text recognition model often requires a large amount of
labeled data, but data labeling can be difficult, expensive, or time-consuming,
especially for Traditional Chinese text recognition. To the best of our
knowledge, public datasets for Traditional Chinese text recognition are
lacking. This paper presents a framework for a Traditional Chinese synthetic
data engine which aims to improve text recognition model performance. We
generated over 20 million synthetic data and collected over 7,000 manually
labeled data TC-STR 7k-word as the benchmark. Experimental results show that a
text recognition model can achieve much better accuracy either by training from
scratch with our generated synthetic data or by further fine-tuning with TC-STR
7k-word.
