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arXiv · 1908.09990

Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning

Xugong Qin·Yu Zhou·Dongbao Yang·Weiping Wang
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Abstract

Detecting curved text in the wild is very challenging. Recently, most state-of-the-art methods are segmentation based and require pixel-level annotations. We propose a novel scheme to train an accurate text detector using only a small amount of pixel-level annotated data and a large amount of data annotated with rectangles or even unlabeled data. A baseline model is first obtained by training with the pixel-level annotated data and then used to annotate unlabeled or weakly labeled data. A novel strategy which utilizes ground-truth bounding boxes to generate pseudo mask annotations is proposed in weakly-supervised learning. Experimental results on CTW1500 and Total-Text demonstrate that our method can substantially reduce the requirement of pixel-level annotated data. Our method can also generalize well across two datasets. The performance of the proposed method is comparable with the state-of-the-art methods with only 10% pixel-level annotated data and 90% rectangle-level weakly annotated data.

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Xugong Qin, Yu Zhou, Dongbao Yang, Weiping Wang. 2019-08-27. Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning. https://arxiv.org/abs/1908.09990

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