What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution

• URL: http://arxiv.org/abs/2602.06450v1
• Date: Fri, 06 Feb 2026 07:23:54 GMT
• Title: What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution
• Authors: Xingsong Ye, Yongkun Du, JiaXin Zhang, Chen Li, Jing LYU, Zhineng Chen,
• Abstract summary: We introduce UnionST, a strong data engine that synthesizes text covering a union of challenging samples.<n>We then construct UnionST-S, a large-scale synthetic dataset with improved simulations in challenging scenarios.<n>Experiments show that models trained on UnionST-S achieve significant improvements over existing synthetic datasets.
• Score: 21.806975276583174
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale and categorical-balanced text data is essential for training effective Scene Text Recognition (STR) models, which is hard to achieve when collecting real data. Synthetic data offers a cost-effective and perfectly labeled alternative. However, its performance often lags behind, revealing a significant domain gap between real and current synthetic data. In this work, we systematically analyze mainstream rendering-based synthetic datasets and identify their key limitations: insufficient diversity in corpus, font, and layout, which restricts their realism in complex scenarios. To address these issues, we introduce UnionST, a strong data engine synthesizes text covering a union of challenging samples and better aligns with the complexity observed in the wild. We then construct UnionST-S, a large-scale synthetic dataset with improved simulations in challenging scenarios. Furthermore, we develop a self-evolution learning (SEL) framework for effective real data annotation. Experiments show that models trained on UnionST-S achieve significant improvements over existing synthetic datasets. They even surpass real-data performance in certain scenarios. Moreover, when using SEL, the trained models achieve competitive performance by only seeing 9% of real data labels.

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