2020/01/30 by Liang Zhang, Zhang, Liang, Yufei Liu +14
Computer Science · Engineering · #Algorithm #Artificial intelligence #Computational complexity theory #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Feature (linguistics) #Feature extraction #Handwritten Text Recognition Techniques #Image Processing and 3D Reconstruction #Machine learning #Mechanism (biology) #Pattern recognition (psychology) #Tower #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.2002.03741
published in arXiv (Cornell University) (Cornell University) · Accepted by ICASSP 2020
arxiv created 2020/01/30 · openalex publication_date 2020/01/30 · arxiv updated 2020/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 multioriented 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.