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Found 4 papers, 3 papers with code
Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors
Existing scene text detection methods typically rely on extensive real data for training.
Self-supervised Character-to-Character Distillation for Text Recognition
Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution.
Self-supervised Implicit Glyph Attention for Text Recognition
Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories.
Ranked #2 on
Scene Text Recognition
on ICDAR 2003
Industrial Scene Text Detection with Refined Feature-attentive Network
Then, an attentive refinement network is developed by the attention map to rectify the location deviation of candidate boxes.
