CASIA OpenIR  > 紫东太初大模型研究中心  > 图像与视频分析
Elite Loss for scene text detection
Zhao X(赵旭)1,2; Zhao CY(赵朝阳)1,2; Guo H(郭海云)1,2; Zhu YS(朱优松)1,2; Tang M(唐明)1,2; Wang JQ(王金桥)1,2
Source PublicationNeurocomputing

Many scene text detection approaches generate foreground segmentation maps to detect the text instances. In these methods, usually all the pixels within the bounding box regions of the text are equally treated as foreground during the training process. However, different from the general object segmentation problem, we argue that not all the pixels across the text bounding box region contribute equally for locating the text instance. Specifically, some in-box not-on-stroke pixels even degrade the detection performance. Moreover, for the segmentation based methods with a regression step applied to predict the corresponding bounding box on each pixel, not all the pixels need to be fully trained to predict foreground texts. Therefore, in this paper, we propose Elite Loss, which is intended to down-weight the contributions of the in-box not-on-stoke pixels while paying more attention to the on-stoke pixels. Furthermore, we design a segmentation-based method to validate the effectiveness of the proposed Elite Loss. Extensive experiments demonstrate that our methods achieve the state-of-the-art results on all three challenging datasets, with the F-score of 0.855 on ICDAR2015, 0.425 on COCO-Text, and 0.819 on MSRA-TD500.

KeywordScene Text Detection 场景文本检测 Elite Loss 精英损失函数 Object Detection 目标检测
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed BySCI
WOS IDWOS:000456834100026
Sub direction classification图像视频处理与分析
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorZhao CY(赵朝阳)
Recommended Citation
GB/T 7714
Zhao X,Zhao CY,Guo H,et al. Elite Loss for scene text detection[J]. Neurocomputing,2019(333):284-291.
APA Zhao X,Zhao CY,Guo H,Zhu YS,Tang M,&Wang JQ.(2019).Elite Loss for scene text detection.Neurocomputing(333),284-291.
MLA Zhao X,et al."Elite Loss for scene text detection".Neurocomputing .333(2019):284-291.
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