TY - GEN
T1 - Margin Guidance Network for Arbitrary-shaped Scene Text Detection
AU - Li, Xin
AU - Wu, Xingjiao
AU - Ma, Tianlong
AU - Zhou, Zhao
AU - Chen, Luhui
AU - He, Liang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Segmentation-based scene text detection approaches have been adopted to arbitrary-shaped texts and have achieved a great progress. However, false detection always easily exist when the arbitrary-shaped texts are close to each other. In this paper, we propose the Margin Guidance Network (MGN) that mainly based on the margin constraint residual module (MCRM) to address aforementioned problem. The MCRM considers the margins between multiple text instance masks to guide the training of network and improve the performance on text detection. The MCRM contains two prediction branch, the one can generate the multiple different scale of masks for a text instance and the other branch is used to generate multiple margins between the above masks. Experimental results on three public benchmarks including ICDAR2015, CTW1500 and Total-Text have demonstrated that the proposed MGN achieves the state-of-The-Art results.
AB - Segmentation-based scene text detection approaches have been adopted to arbitrary-shaped texts and have achieved a great progress. However, false detection always easily exist when the arbitrary-shaped texts are close to each other. In this paper, we propose the Margin Guidance Network (MGN) that mainly based on the margin constraint residual module (MCRM) to address aforementioned problem. The MCRM considers the margins between multiple text instance masks to guide the training of network and improve the performance on text detection. The MCRM contains two prediction branch, the one can generate the multiple different scale of masks for a text instance and the other branch is used to generate multiple margins between the above masks. Experimental results on three public benchmarks including ICDAR2015, CTW1500 and Total-Text have demonstrated that the proposed MGN achieves the state-of-The-Art results.
KW - Margin Guidance Network
KW - Scene text detection
KW - arbitrary-shaped text
UR - https://www.scopus.com/pages/publications/85098753499
U2 - 10.1109/ICTAI50040.2020.00169
DO - 10.1109/ICTAI50040.2020.00169
M3 - 会议稿件
AN - SCOPUS:85098753499
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 1111
EP - 1117
BT - Proceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
A2 - Alamaniotis, Miltos
A2 - Pan, Shimei
PB - IEEE Computer Society
T2 - 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
Y2 - 9 November 2020 through 11 November 2020
ER -