Summary of Dntextspotter: Arbitrary-shaped Scene Text Spotting Via Improved Denoising Training, by Yu Xie et al.
DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training
by Yu Xie, Qian Qiao, Jun Gao, Tianxiang Wu, Jiaqing Fan, Yue Zhang, Jielei Zhang, Huyang Sun
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel denoising training method called DNTextSpotter for arbitrary-shaped text spotting tasks. The current state-of-the-art end-to-end text spotting methods based on Transformer architecture rely on bipartite graph matching, which can be unstable and affect the model’s performance. To address this issue, the authors decompose the queries into noised positional queries and noised content queries using Bezier control points and masked character sliding methods, respectively. Additionally, an extra loss function for background characters classification is employed to improve the model’s perception of the background. The proposed method outperforms state-of-the-art approaches on four benchmarks, including a 11.3% improvement in the Inverse-Text dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train text spotting models that can recognize text in irregular shapes and complex scenes. This is important because current methods have limitations when it comes to recognizing text in real-world scenarios. The authors use a special type of training called denoising, which helps the model learn from noisy data. They also add an extra step to help the model understand the background better. As a result, their method performs better than others on four different datasets. |
Keywords
» Artificial intelligence » Classification » Loss function » Transformer
