A Single-Shot Oriented Scene Text Detector With Learnable Anchors
Sheng, Fenfen1,2; Chen, Zhineng1; Mei, Tao3; Xo, Bo1
2019-07
会议名称IEEE International Conference on Multimedia and Expo
会议日期2019-7-8 ~ 2019-7-12
会议地点Shanghai, China
摘要

Current regression based text detectors mainly use fixed anchors, where scales and positions cannot be changed during network training. As scene texts tend to have large variation in orientations, aspect ratios and sizes, fixed anchors are insufficient to cover all varieties. This paper proposes a novel text detector with learnable anchors, named LATD. LATD contains two prediction branches. One aims to refine scales and locations of anchors according to the characteristics of scene texts. The other one receives refined anchors as defaults and regresses their offsets to text regions. These two branches are optimized jointly without sacrifices much speed. Meanwhile, we explore the class-imbalance issue between texts and backgrounds, and replace softmax loss with focal loss. Extensive experiments on both oriented and horizontal benchmarks demonstrate the effectiveness of LATD with new state-of-the-art performance. By visualizing qualitative results, as expected, LATD provides more accurate locations and lower rate of missed detections.

七大方向——子方向分类文字识别与文档分析
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/39270
专题数字内容技术与服务研究中心_听觉模型与认知计算
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.JD AI Research
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Sheng, Fenfen,Chen, Zhineng,Mei, Tao,et al. A Single-Shot Oriented Scene Text Detector With Learnable Anchors[C],2019.
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