Arbitrary-Shaped Scene Text Detection with Scoring Mask Quality
Xinyu Liu, Xiaoqian Liu, Bin Wang, Xin Luo, Xin-Shun Xu · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Recently, segmentation based text detection methods have achieved outstanding results. However, due to the complexity of natural scenes and the diversity of text shapes, many instance segmentation based methods suffer from serious false positive problems. One of the main reasons is that these methods regard a proposal as text only depending on its classification score. But in practice, a highquality proposal should not only be classified accurately, but more importantly, be positioned accurately. To solve the problem, we propose an arbitrary-shaped text detection network with Mask Scoring quality (MSNet), which measures the quality of a proposal jointly considering its classification score and mask score, so as to filter out FP samples. Extensive experiments on 4 real-world datasets demonstrate that MSNet is able to achieve better results than some state-of-the-art methods.