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基于单个字符的嵌入特征聚合使用交叉注意对于场景文本超分辨率
Meng Wang1, Qianqian Li1, Haipeng Liu1
1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了一种用于场景文本超分辨率 (STSR) 的新方法,使用单字符嵌入和交叉注意力. 该方法提高了复杂背景中的文本可读性,提高了基准的识别精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 场景文本超分辨率 (STSR) 旨在提高文本质量,以提高可读性和下游任务.
- 在STSR中的挑战包括角色模糊性和复杂背景的干扰,特别是与紧密相连的角色.
研究的目的:
- 提出一种基于单个字符的嵌入功能聚合方法,使用交叉注意力用于场景文本超分辨率 (SCE-STISR).
- 为应对复杂场景文本图像中角色模糊性和背景干扰的挑战.
主要方法:
- 采用动态特征提取机制,具有可适应的多尺度特征重量.
- 引入了一种双层交叉注意机制,用于将单个字符的特征与文本priors汇总在一起,并将视觉语义信息对齐.
- 应用自适应性正常化色彩校正,以减少背景引起的色彩扭曲.
主要成果:
- 在TextZoom基准测试中比基线TATT提高了0.9-1.4%的文本识别准确度.
- 在TextZoom上获得了0.7951的最佳SSIM值和21.84的PSNR.
- 在五个文本识别数据集上,与现有的基线相比,准确度有0.2-2.2%的改进.
结论:
- 拟议的SCE-STISR方法通过解决字符模糊性和背景干扰,有效地提高场景文本超分辨率.
- 该方法验证了单个字符嵌入聚合和交叉注意力的有效性,用于在具有挑战性的场景中提高文本识别精度.
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