  Text segmentation is a challenging vision task with many downstream
applications. Current text segmentation methods require pixel-level
annotations, which are expensive in the cost of human labor and limited in
application scenarios. In this paper, we take the first attempt to perform
weakly-supervised text instance segmentation by bridging text recognition and
text segmentation. The insight is that text recognition methods provide precise
attention position of each text instance, and the attention location can feed
to both a text adaptive refinement head (TAR) and a text segmentation head.
Specifically, the proposed TAR generates pseudo labels by performing two-stage
iterative refinement operations on the attention location to fit the accurate
boundaries of the corresponding text instance. Meanwhile, the text segmentation
head takes the rough attention location to predict segmentation masks which are
supervised by the aforementioned pseudo labels. In addition, we design a
mask-augmented contrastive learning by treating our segmentation result as an
augmented version of the input text image, thus improving the visual
representation and further enhancing the performance of both recognition and
segmentation. The experimental results demonstrate that the proposed method
significantly outperforms weakly-supervised instance segmentation methods on
ICDAR13-FST (18.95$\%$ improvement) and TextSeg (17.80$\%$ improvement)
benchmarks.
