TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition

• URL: http://arxiv.org/abs/2412.01137v2
• Date: Wed, 10 Sep 2025 07:03:33 GMT
• Title: TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
• Authors: Xingsong Ye, Yongkun Du, Yunbo Tao, Zhineng Chen,
• Abstract summary: Scene text recognition (STR) suffers from challenges of either less realistic synthetic training data or the difficulty of collecting sufficient real-world data.<n>We introduce TextSSR: a novel pipeline for Synthesizing Scene Text Recognition training data.<n>It achieves accuracy through a proposed region-centric text generation with position-glyph enhancement.<n>It maintains realism by guiding style and appearance generation using contextual hints from surrounding text or background.
• Score: 19.566553192778525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene text recognition (STR) suffers from challenges of either less realistic synthetic training data or the difficulty of collecting sufficient high-quality real-world data, limiting the effectiveness of trained models. Meanwhile, despite producing holistically appealing text images, diffusion-based visual text generation methods struggle to synthesize accurate and realistic instance-level text at scale. To tackle this, we introduce TextSSR: a novel pipeline for Synthesizing Scene Text Recognition training data. TextSSR targets three key synthesizing characteristics: accuracy, realism, and scalability. It achieves accuracy through a proposed region-centric text generation with position-glyph enhancement, ensuring proper character placement. It maintains realism by guiding style and appearance generation using contextual hints from surrounding text or background. This character-aware diffusion architecture enjoys precise character-level control and semantic coherence preservation, without relying on natural language prompts. Therefore, TextSSR supports large-scale generation through combinatorial text permutations. Based on these, we present TextSSR-F, a dataset of 3.55 million quality-screened text instances. Extensive experiments show that STR models trained on TextSSR-F outperform those trained on existing synthetic datasets by clear margins on common benchmarks, and further improvements are observed when mixed with real-world training data. Code is available at https://github.com/YesianRohn/TextSSR.

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