Weakly-Supervised Arbitrary-Shaped Text Detection with Expectation-Maximization Algorithm

• URL: http://arxiv.org/abs/2012.00424v1
• Date: Tue, 1 Dec 2020 11:45:39 GMT
• Title: Weakly-Supervised Arbitrary-Shaped Text Detection with Expectation-Maximization Algorithm
• Authors: Mengbiao Zhao, Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu
• Abstract summary: We study weakly-supervised arbitrary-shaped text detection for combining various weak supervision forms. We propose an Expectation-Maximization (EM) based weakly-supervised learning framework to train an accurate arbitrary-shaped text detector. Our method yields comparable performance to state-of-the-art methods on three benchmarks.
• Score: 35.0126313032923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Arbitrary-shaped text detection is an important and challenging task in computer vision. Most existing methods require heavy data labeling efforts to produce polygon-level text region labels for supervised training. In order to reduce the cost in data labeling, we study weakly-supervised arbitrary-shaped text detection for combining various weak supervision forms (e.g., image-level tags, coarse, loose and tight bounding boxes), which are far easier for annotation. We propose an Expectation-Maximization (EM) based weakly-supervised learning framework to train an accurate arbitrary-shaped text detector using only a small amount of polygon-level annotated data combined with a large amount of weakly annotated data. Meanwhile, we propose a contour-based arbitrary-shaped text detector, which is suitable for incorporating weakly-supervised learning. Extensive experiments on three arbitrary-shaped text benchmarks (CTW1500, Total-Text and ICDAR-ArT) show that (1) using only 10% strongly annotated data and 90% weakly annotated data, our method yields comparable performance to state-of-the-art methods, (2) with 100% strongly annotated data, our method outperforms existing methods on all three benchmarks. We will make the weakly annotated datasets publicly available in the future.

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