Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors

Tongkun Guan; Wei Shen; Xue Yang; Xuehui Wang; Xiaokang Yang

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arXiv · 2312.05286

Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors

Tongkun Guan·Wei Shen·Xue Yang·Xuehui Wang·Xiaokang Yang

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Abstract

Existing scene text detection methods typically rely on extensive real data for training. Due to the lack of annotated real images, recent works have attempted to exploit large-scale labeled synthetic data (LSD) for pre-training text detectors. However, a synth-to-real domain gap emerges, further limiting the performance of text detectors. Differently, in this work, we propose FreeReal, a real-domain-aligned pre-training paradigm that enables the complementary strengths of both LSD and unlabeled real data (URD). Specifically, to bridge real and synthetic worlds for pre-training, a glyph-based mixing mechanism (GlyphMix) is tailored for text images.GlyphMix delineates the character structures of synthetic images and embeds them as graffiti-like units onto real images. Without introducing real domain drift, GlyphMix freely yields real-world images with annotations derived from synthetic labels. Furthermore, when given free fine-grained synthetic labels, GlyphMix can effectively bridge the linguistic domain gap stemming from English-dominated LSD to URD in various languages. Without bells and whistles, FreeReal achieves average gains of 1.97%, 3.90%, 3.85%, and 4.56% in improving the performance of FCENet, PSENet, PANet, and DBNet methods, respectively, consistently outperforming previous pre-training methods by a substantial margin across four public datasets. Code is available at https://github.com/SJTU-DeepVisionLab/FreeReal.

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BibTeX RIS

Tongkun Guan, Wei Shen, Xue Yang, Xuehui Wang, Xiaokang Yang. 2024-07-10. Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors. https://arxiv.org/abs/2312.05286

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