Conference paper
Efficient scene text detection with textual attention tower
IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP) 2020 (Barcelona, Spain, 04/05/2020–08/05/2020)
2020
Abstract
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.
Details
- Title
- Efficient scene text detection with textual attention tower
- Authors/Creators
- L. Zhang (Author/Creator) - Xidian UniversityY. Liu (Author/Creator) - Xidian UniversityH. Xiao (Author/Creator) - OrionStar Ltd.,ChinaL. Yang (Author/Creator) - Xidian UniversityG. Zhu (Author/Creator) - Xidian UniversityS.A.A. Shah (Author/Creator) - Murdoch UniversityM. Bennamoun (Author/Creator) - The University of Western AustraliaP. Shen (Author/Creator) - Xidian University
- Conference
- IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP) 2020 (Barcelona, Spain, 04/05/2020–08/05/2020)
- Identifiers
- 991005544170607891
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
Metrics
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