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\u2013RPN, RRPN and FPN\u2013RRPN), 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Zhuoyao Zhong","user_id":"41871"},{"type":"user_nicename","value":"Lei Sun","user_id":"32740"},{"type":"user_nicename","value":"Qiang Huo","user_id":"33297"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"International Journal on Document Analysis and 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