ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
This work addresses the problem of text-image alignment in OCR for researchers and practitioners, offering a novel pre-training approach that is incremental in advancing existing joint pre-training techniques.
The paper tackles the challenge of aligning text instances with their corresponding regions in images for OCR tasks by proposing ODM, a pre-training method that transfers diverse text styles to a uniform style, resulting in significant performance improvements on multiple public datasets 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.