# Traditional Chinese Synthetic Datasets Verified with Labeled Data for Scene Text Recognition

arXiv: https://arxiv.org/abs/2111.13327 · HTML: https://mlsanity.com/paper/2111.13327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition](https://mlsanity.com/paper/2003.06606/) (2020)

## Built on by (most cited first, 1 of 1)

- [SMILE: Sequence-to-Sequence Domain Adaption with Minimizing Latent Entropy for Text Image Recognition](https://mlsanity.com/paper/2202.11949/) (2022)

## Abstract (arXiv)

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.