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

## TL;DR

This paper introduces the COCO_TS dataset with pixel-level annotations derived from weak supervision, enabling improved scene text segmentation without relying solely on synthetic data, thus better capturing real-world complexity.

## Contribution

The creation of the COCO_TS dataset with pixel-level annotations from weak supervision for scene text segmentation is a novel contribution.

## Key findings

- Using COCO_TS improves segmentation performance over synthetic data.
- The dataset allows training with fewer samples while maintaining high accuracy.
- Weak supervision effectively bridges the gap between synthetic and real data.

## Abstract

The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances.