论文标题
场景文本检测与选定的锚
Scene Text Detection with Selected Anchor
论文作者
论文摘要
使用密集锚定方案进行场景文本检测的对象建议技术经常应用以获得高度召回。它导致准确性的显着提高，但浪费了计算搜索，回归和分类。在本文中，我们使用有效的选定锚点代替密集的锚来提取基于锚定选择的区域建议网络（AS-RPN）来提取文本建议。锚点的中心，尺度，纵横比和方向是可以学习的，而不是固定，这导致了很高的召回，并且大大减少了锚的数量。通过更快的RCNN替换基于锚的RPN，基于AS-RPN的RCNN可以与先前最先进的文本检测方法相当的性能，在标准基准上的先前最先进的文本检测方法，包括可可TEXT，ICDAR2013，ICDAR2015，ICDAR2015，ICDAR2015和MSRA-TD500时使用单尺度和单个模型和单个模型（仅使用单个模型）。
Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.
