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

arXiv: https://arxiv.org/abs/1904.00818 · HTML: https://mlsanity.com/paper/1904.00818/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 12 of 12)

- [Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation](https://mlsanity.com/paper/2401.17904/) (2024)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- [WAS: Dataset and Methods for Artistic Text Segmentation](https://mlsanity.com/paper/2408.00106/) (2024)
- [Weakly-Supervised Text Instance Segmentation](https://mlsanity.com/paper/2303.10848/) (2023)
- [EAFormer: Scene Text Segmentation with Edge-Aware Transformers](https://mlsanity.com/paper/2407.17020/) (2024)
- [ENCONTER: Entity Constrained Progressive Sequence Generation via Insertion-based Transformer](https://mlsanity.com/paper/2103.09548/) (2021)
- [ConText: Driving In-context Learning for Text Removal and Segmentation](https://mlsanity.com/paper/2506.03799/) (2025)
- [AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation](https://mlsanity.com/paper/2103.16190/) (2021)
- [Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions](https://mlsanity.com/paper/2308.13178/) (2023)
- [BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation](https://mlsanity.com/paper/2108.09433/) (2021)
- [A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition](https://mlsanity.com/paper/2503.15639/) (2025)
- [Learning Attribute-aware Representations for Few-shot Scene Text Segmentation](https://mlsanity.com/paper/2504.11164/) (2025)

## Abstract (arXiv)

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.