@inproceedings{
qiao2024dntextspotter,
title={{DNT}extSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training},
author={Qian Qiao and Yu Xie and Jun Gao and Tianxiang Wu and Shaoyao Huang and Jiaqing Fan and Ziqiang Cao and Zili Wang and Yue Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=A8WcL2WBIh}
}
More and more end-to-end text spotting methods based on Transformer architecture have demonstrated superior performance. These methods utilize a bipartite graph matching algorithm to perform one-to-one optimal matching between predicted objects and actual objects. However, the instability of bipartite graph matching can lead to inconsistent optimization targets, thereby affecting the training performance of the model. Existing literature applies denoising training to solve the problem of bipartite graph matching instability in object detection tasks. Unfortunately, this denoising training method cannot be directly applied to text spotting tasks, as these tasks need to perform irregular shape detection tasks and more complex text recognition tasks than classification. To relieve the issue, in this paper, we propose a novel denoising training method (DNTextSpotter) for arbitrary-shaped text detection and recognition. Specifically, we decompose the queries of the denoising part into noised positional queries and noised content queries. We use the four Bezier control points of the Bezier center curve to generate the noised positional queries. For the noised content queries, considering that the output of the text in a fixed positional order is not conducive to aligning position with content, we employ a masked character sliding method to initialize content queries to assist the alignment of text content and position. To improve the model's perception of the background, we further utilize an additional loss function for background characters classification in the denoising training part. Although DNTextSpotter is conceptually simple, it outperforms the state-of-the-art methods on four benchmarks (Total-Text, SCUT-CTW1500, ICDAR15, and Inverse-Text), especially yielding an improvement of $11.3\%$ against the best approach in Inverse-Text.
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training
Qian Qiao Yu Xie Jun Gao Tianxiang Wu Shaoyao Huang Jiaqing Fan Ziqiang Cao Zili Wang Yue Zhang