# TextCohesion: Detecting Text for Arbitrary Shapes

arXiv: https://arxiv.org/abs/1904.12640 · HTML: https://mlsanity.com/paper/1904.12640/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [End-to-End Video Text Spotting with Transformer](https://mlsanity.com/paper/2203.10539/) (2022)
- [Explore Faster Localization Learning For Scene Text Detection](https://mlsanity.com/paper/2207.01342/) (2022)
- [FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation](https://mlsanity.com/paper/2305.03327/) (2023)
- [Real-time End-to-End Video Text Spotter with Contrastive Representation Learning](https://mlsanity.com/paper/2207.08417/) (2022)
- [DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text](https://mlsanity.com/paper/2312.01938/) (2023)

## Abstract (arXiv)

In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions. These components are easier to handle than the entire text instance. A confidence scoring mechanism is designed to filter characters that are similar to text. Our method can integrate text contexts intensively when backgrounds are complex. Experiments on two curved challenging benchmarks demonstrate that TextCohesion outperforms state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and bfseries86.3% on SCUT-CTW1500.