2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-8.6
Paper Title FC2RN: A FULLY CONVOLUTIONAL CORNER REFINEMENT NETWORK FOR ACCURATE MULTI-ORIENTED SCENE TEXT DETECTION
Authors Xugong Qin, Yu Zhou, Youhui Guo, Dayan Wu, Weiping Wang, Institute of Information Engineering, Chinese Academy of Sciences, China
SessionMMSP-8: Multimedia Retrieval and Signal Detection
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Multimedia Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Accurate detection of multi-oriented text that accounts for a large proportion in real practice is of great significance. The performance has improved rapidly on common benchmarks in recent years. However, dense long text case and the quality of detection are easy to be overlooked. Direct regression may produce low-quality and incomplete detections due to the constrain of the receptive field; proposal-based methods could alleviate this but might introduce redundant context due to RoI operation, degrading the performance. To address the dilemma, a novel proposed corner-aware convolution in which the sampling positions tightly cover the text area is utilized to encode an initial corner prediction into the feature maps, which can be further used to produce a refined corner prediction. We embed the proposed module into an anchor-free baseline model, leading to a simple and effective fully convolutional corner refinement network (FC2RN). Experimental results on four public datasets including MSRA-TD500, ICDAR2015, RCTW-17, and COCO-Text demonstrate that FC2RN can outperform state-of-the-art methods.