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.
@article{arxiv.2111.13327,
title = {Traditional Chinese Synthetic Datasets Verified with Labeled Data for Scene Text Recognition},
author = {Yi-Chang Chen and Yu-Chuan Chang and Yen-Cheng Chang and Yi-Ren Yeh},
journal= {arXiv preprint arXiv:2111.13327},
year = {2022}
}