The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its Intersection-over-Union-based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with the anchor-based region proposal generation approaches (e.g., RPN, FPN–RPN, RRPN and FPN–RRPN), AF-RPN can get rid of complicated anchor design and achieves higher recall rate on both horizontal and multi-oriented text detection benchmark tasks. Owing to the high-quality text proposals, our Faster R-CNN-based two-stage text detection approach achieves the state-of-the-art results on ICDAR-2017 MLT, COCO-Text, ICDAR-2015 and ICDAR-2013 text detection benchmark tasks by only using single-scale and single-model testing.

中文翻译：

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无锚区域建议网络，用于基于R-CNN的更快文本检测方法

Faster R-CNN和SSD框架的锚定机制被认为不足以进行场景文本检测，这可以归因于其基于锚点与地面实况框之间的“基于联合的交集”的匹配标准。为了更好地封装各种形状的场景文本实例，需要手动设计各种比例，纵横比甚至方向的锚点，这使得基于锚点的方法复杂且效率低下。在本文中，我们提出了一种新颖的无锚区域提议网络（AF-RPN），以取代Faster R-CNN框架中原始的基于锚的RPN，以解决上述问题。与基于锚点的区域提案生成方法（例如RPN，FPN-RPN，RRPN和FPN-RRPN）相比，AF-RPN可以摆脱复杂的锚设计，并且在水平和多方向文本检测基准测试任务上都可以实现更高的召回率。基于高质量的文本建议，我们基于Faster R-CNN的两阶段文本检测方法在ICDAR-2017 MLT，COCO-Text，ICDAR-2015和ICDAR-2013文本上实现了最新的结果仅使用单尺度和单模型测试来检测基准测试任务。