ODM：一种面向场景文本检测与识别定位的文本-图像进一步对齐预训练方法
ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
AI总结：
针对OCR任务中文本与图像文本区域对齐难、标注成本高的问题，提出ODM预训练方法结合Text-Controller模块，实现更优文本对齐并利用无标注数据，在场景文本检测与识别定位任务上性能优于现有预训练方法。
摘要：
近年来，文本-图像联合预训练技术已在各类任务中展现出可观的效果。然而在光学字符识别（OCR）任务中，将文本实例与图像中对应的文本区域对齐仍是一项挑战，因为这需要实现文本与OCR-Text（我们将图像中的文本称为OCR-Text，以区分自然语言中的文本）之间的有效对齐，而非对整体图像内容的全局理解。本文提出了一种名为OCR-Text去风格化建模（OCR-Text Destylization Modeling，ODM）的新型预训练方法，该方法可基于文本提示将图像中各类风格的文本转换为统一风格。借助ODM，我们实现了文本与OCR-Text之间更优的对齐，使预训练模型能够适应场景文本检测与识别定位任务复杂多样的风格。此外，我们专门为ODM设计了一种全新的标注生成方法，并将其与我们提出的Text-Controller模块相结合，以解决OCR任务中的标注成本难题，让更多无标注数据能够参与预训练。在多个公开数据集上开展的大量实验表明，我们的方法显著提升了性能，在场景文本检测与识别定位任务上优于现有预训练方法。代码可在https://github.com/PriNing/ODM获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
