  Providing pixel-level supervisions for scene text segmentation is inherently
difficult and costly, so that only few small datasets are available for this
task. To face the scarcity of training data, previous approaches based on
Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for
pre-training. However, synthetic data cannot reproduce the complexity and
variability of natural images. In this work, we propose to use a weakly
supervised learning approach to reduce the domain-shift between synthetic and
real data. Leveraging the bounding-box supervision of the COCO-Text and the MLT
datasets, we generate weak pixel-level supervisions of real images. In
particular, the COCO-Text-Segmentation (COCO_TS) and the MLT-Segmentation
(MLT_S) datasets are created and released. These two datasets are used to train
a CNN, the Segmentation Multiscale Attention Network (SMANet), which is
specifically designed to face some peculiarities of the scene text segmentation
task. The SMANet is trained end-to-end on the proposed datasets, and the
experiments show that COCO_TS and MLT_S are a valid alternative to synthetic
images, allowing to use only a fraction of the training samples and improving
significantly the performances.
