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About this paper
Tianlun Zheng, Zhineng Chen, Shancheng Fang, Hongtao Xie, Yu-Gang Jiang
CDistNet introduces a framework for robust scene text recognition by perceiving multi-domain character distances, deeply integrating visual, semantic, and positional information. This approach demonstrates superior accuracy on highly irregular, real-world, and multilingual text datasets compared to existing methods.
Key insights
- Allowing position embeddings to simultaneously query and fuse information from both visual and semantic domains significantly improves feature-character alignment by modeling 'multi-domain character distance'.
- Dynamically reinforcing position embeddings through self-attention (SAE) transforms them into 'content-aware receptors,' enhancing their utility beyond static placeholders for diverse character patterns.
- Sequential (autoregressive) decoding methods maintain superior accuracy in severely deformed scenarios compared to popular parallel decoding schemes, indicating a robustness advantage in extreme cases.
Summary by alphaXiv.
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