HGR-Net:用于任意形状场景文本检测的层次图形推理网络
概括
这项研究介绍了一种层次图形推理网络 (HGR-Net),用于挑战场景文本检测. 通过整合实例级和组件级信息,HGR-Net可以有效地检测任意形状的文本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 场景文本检测对于文本阅读至关重要,但由于文本的变化,仍然具有挑战性.
- 现有的方法通常使用自下而上的组件提取或自上而下的轮回归.
- 需要一种混合方法来处理不同的文本形状和方向.
研究的目的:
- 提出一种新的等级图形推理网络 (HGR-Net),用于任意形状的场景文本检测.
- 开发一种整合实例级和组件级文本表示的方法.
- 为了提高场景文本检测的准确性和稳定性.
主要方法:
- 推出了HGR-Net与文本特征提取网络 (TFEN) 和文本关系学习网络 (TRLN).
- TFEN提取多粒度的候选文本 (实例级轮,组件级四角形).
- TRLN使用文本间和文本内图表进行上下文和几何推理,弥合层次层次.
主要成果:
- HGR-Net在任意定位和形状场景文本检测方面展示了最先进的性能.
- 在SCUT-CTW1500,总文本和ICDAR15基准测试中取得了卓越的结果.
- 层次关系推理有效地利用互补的图形嵌入.
结论:
- 对于复杂的场景文本检测,HGR-Net提供了一种强大的混合方法.
- 提出的等级图形推理有效地捕捉了复杂的文本结构.
- 这种方法在任意形状场景文本检测方面推进了最先进的技术.
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