论文标题
有效的场景文本检测具有文本注意力塔
Efficient Scene Text Detection with Textual Attention Tower
论文作者
论文摘要
场景文本检测多年来一直受到关注，并在各种基准测试中取得了令人印象深刻的表现。在这项工作中，我们提出了一种有效，准确的方法，以检测场景图像中的多个文本。提出的功能融合机制使我们能够使用较浅的网络来降低计算复杂性。采用自我注意的机制来抑制假阳性检测。在包括ICDAR 2013，ICDAR 2015和MSRA-TD500在内的公共基准测试的实验表明，我们提出的方法可以实现更好或可比较的性能，而参数较少，计算成本较少。
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.
