ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
<|reference_start|>ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting: In recent years, text-image joint pre-training techniques have shown promising results in various tasks. However, in Optical Character Recognition (OCR) tasks, aligning text instances with their corresponding text regions in images poses a challenge, as it requires effective alignment between text and OCR-Text (referring to the text in images as OCR-Text to distinguish from the text in natural language) rather than a holistic understanding of the overall image content. In this paper, we propose a new pre-training method called OCR-Text Destylization Modeling (ODM) that transfers diverse styles of text found in images to a uniform style based on the text prompt. With ODM, we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks. Additionally, we have designed a new labeling generation method specifically for ODM and combined it with our proposed Text-Controller module to address the challenge of annotation costs in OCR tasks, allowing a larger amount of unlabeled data to participate in pre-training. Extensive experiments on multiple public datasets demonstrate that our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks. Code is available at https://github.com/PriNing/ODM.<|reference_end|>
@article{duan2024odm:,
    title={ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text
  Detection and Spotting},
    author={Chen Duan and Pei Fu and Shan Guo and Qianyi Jiang and Xiaoming Wei},
    journal={arXiv preprint arXiv:2403.00303},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00303},
    primaryClass={cs.CV}
}