  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.
