CCDPlus : Towards Accurate Character to Character Distillation for Text Recognition
Existing scene text recognition methods leverage large-scale labeled synthetic data (LSD) to reduce reliance on labor-intensive annotation tasks and improve recognition capability in real-world scenarios. However, the emergence of a synth-to-real domain gap still limits their efficiency and robustne...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 5 vom: 26. Mai, Seite 3546-3562 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2025
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Résumé: | Existing scene text recognition methods leverage large-scale labeled synthetic data (LSD) to reduce reliance on labor-intensive annotation tasks and improve recognition capability in real-world scenarios. However, the emergence of a synth-to-real domain gap still limits their efficiency and robustness. Consequently, harvesting the meaningful intrinsic qualities of unlabeled real data (URD) is of great importance, given the prevalence of text-laden images. Toward the target, recent efforts have focused on pre-training on URD through sequence-to-sequence self-supervised learning, followed by fine-tuning on LSD via supervised learning. Nevertheless, they encounter three important issues: coarse representation learning units, inflexible data augmentation, and an emerging real-to-synth domain drift. To overcome these challenges, we propose CCDPlus, an accurate character-to-character distillation method for scene text recognition with a joint supervised and self-supervised learning framework. Specifically, tailored for text images, CCDPlus delineates the fine-grained character structures on URD as representation units by transferring knowledge learned from LSD online. Without requiring extra bounding box or pixel-level annotations, this process allows CCDPlus to enable character-to-character distillation flexibly with versatile data augmentation, which effectively extracts general real-world character-level feature representations. Meanwhile, the unified framework combines self-supervised learning on URD with supervised learning on LSD, effectively solving the domain inconsistency and enhancing the recognition performance. Extensive experiments demonstrate that CCDPlus outperforms previous state-of-the-art (SOTA) supervised, semi-supervised, and self-supervised methods by an average of 1.8%, 0.6%, and 1.1% on standard datasets, respectively. Additionally, it achieves a 6.1% improvement on the more challenging Union14M-L dataset |
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| Description: | Date Revised 09.04.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2025.3533737 |