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Semantic-Aware Video Text Detection
Wei Feng1,2; Fei Yin1,2; Xu-Yao Zhang1,2; Cheng-Lin Liu1,2
Conference NameInternaltional Conference on Computer Vision and Pattern Recogintion
Conference Date2021.6.19
Conference Place线上会议

Most existing video text detection methods track texts with appearance features, which are easily influenced by the change of perspective and illumination. Compared with appearance features, semantic features are more robust cues for matching text instances. In this paper, we propose an end-to-end trainable video text detector that tracks texts based on semantic features. First, we introduce a new character center segmentation branch to extract semantic features, which encode the category and position of characters. Then we propose a novel appearance-semantic-geometry descriptor to track text instances, in which semantic features can improve the robustness against appearance changes. To overcome the lack of character-level annotations, we propose a novel weakly-supervised character center detection module, which \textbf{only uses word-level annotated real images} to generate character-level labels. The proposed method achieves state-of-the-art performance on three video text benchmarks ICDAR 2013 Video, Minetto and RT-1K, and two Chinese scene text benchmarks CASIA10K and MSRA-TD500.

Indexed ByEI
Document Type会议论文
Affiliation1.National Laboratory of Pattern Recognition, Institutaion of Automation of Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.CAS Center for Excellence of Brain Science and Intelligence Technology
Recommended Citation
GB/T 7714
Wei Feng,Fei Yin,Xu-Yao Zhang,et al. Semantic-Aware Video Text Detection[C],2021.
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