QQ客服 官方微博 反馈留言

	FOTS: Fast Oriented Text Spotting with a Unified Network
	Xuebo Liu; Ding Liang; Shi Yan; Dagui Chen; Yu Qiao; Junjie Yan
	2018
会议日期	2018
会议地点	美国
英文摘要	Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specially, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method learns more generic features to make our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.
URL标识	查看原文
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13687]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Xuebo Liu,Ding Liang,Shi Yan,et al. FOTS: Fast Oriented Text Spotting with a Unified Network[C]. 见:. 美国. 2018.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。