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The absence of large scale datasets with pixel-level supervisions is asignificant obstacle for the training of deep convolutional networks for scenetext segmentation. For this reason, synthetic data generation is normallyemployed to enlarge the training dataset. Nonetheless, synthetic data cannotreproduce the complexity and variability of natural images. In this paper, aweakly supervised learning approach is used to reduce the shift betweentraining on real and synthetic data. Pixel-level supervisions for a textdetection dataset (i.e. where only bounding-box annotations are available) aregenerated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, whichprovides pixel-level supervisions for the COCO-Text dataset, is created andreleased. The generated annotations are used to train a deep convolutionalneural network for semantic segmentation. Experiments show that the proposeddataset can be used instead of synthetic data, allowing us to use only afraction of the training samples and significantly improving the performances.

COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation