TY - GEN
T1 - A Fusion Strategy for the Single Shot Text Detector
AU - Yu, Zheng
AU - Lyu, Shujing
AU - Lu, Yue
AU - Wang, Patrick S.P.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/26
Y1 - 2018/11/26
N2 - In this paper, we propose a new fusion strategy for scene text detection. The system is based on a single fully convolution network, which outputs the coordinates of text bounding boxes at multiple scales. We improve the performance of text detection by combining a fusion strategy. This strategy obtains precise text bounding boxes according to the confidence of candidate text boxes. It exhibits promising robustness and discriminative power by fusing text boxes. Experimental results on ICDAR2011 and ICDAR2013 datasets indicate the effectiveness and robustness of the proposed fusion strategy with an F-measure of 87%, which outperforms the base network 2%.
AB - In this paper, we propose a new fusion strategy for scene text detection. The system is based on a single fully convolution network, which outputs the coordinates of text bounding boxes at multiple scales. We improve the performance of text detection by combining a fusion strategy. This strategy obtains precise text bounding boxes according to the confidence of candidate text boxes. It exhibits promising robustness and discriminative power by fusing text boxes. Experimental results on ICDAR2011 and ICDAR2013 datasets indicate the effectiveness and robustness of the proposed fusion strategy with an F-measure of 87%, which outperforms the base network 2%.
UR - https://www.scopus.com/pages/publications/85059764564
U2 - 10.1109/ICPR.2018.8545482
DO - 10.1109/ICPR.2018.8545482
M3 - 会议稿件
AN - SCOPUS:85059764564
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3687
EP - 3691
BT - 2018 24th International Conference on Pattern Recognition, ICPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Conference on Pattern Recognition, ICPR 2018
Y2 - 20 August 2018 through 24 August 2018
ER -