# TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection

arXiv: https://arxiv.org/abs/2008.04851 · HTML: https://mlsanity.com/paper/2008.04851/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 30 of 37)

- [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2104.10442/) (2021)
- [SPTS v2: Single-Point Scene Text Spotting](https://mlsanity.com/paper/2301.01635/) (2023)
- [SPTS: Single-Point Text Spotting](https://mlsanity.com/paper/2112.07917/) (2021)
- [Turning a CLIP Model into a Scene Text Detector](https://mlsanity.com/paper/2302.14338/) (2023)
- [Arbitrary Shape Text Detection via Boundary Transformer](https://mlsanity.com/paper/2205.05320/) (2022)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation](https://mlsanity.com/paper/2110.12826/) (2021)
- [Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection](https://mlsanity.com/paper/2109.03426/) (2021)
- [Kernel Proposal Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2203.06410/) (2022)
- [I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection](https://mlsanity.com/paper/2108.01343/) (2021)
- [TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask](https://mlsanity.com/paper/2206.13381/) (2022)
- [Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning](https://mlsanity.com/paper/2308.07202/) (2023)
- [Text Growing on Leaf](https://mlsanity.com/paper/2209.03016/) (2022)
- [Explore Faster Localization Learning For Scene Text Detection](https://mlsanity.com/paper/2207.01342/) (2022)
- [Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors](https://mlsanity.com/paper/2312.05286/) (2023)
- [Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling](https://mlsanity.com/paper/2401.03637/) (2024)
- [LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network](https://mlsanity.com/paper/2306.15142/) (2023)
- [Seeing Text in the Dark: Algorithm and Benchmark](https://mlsanity.com/paper/2404.08965/) (2024)
- [All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2106.12720/) (2021)
- [Zoom Text Detector](https://mlsanity.com/paper/2209.03014/) (2022)
- [Arbitrary Shape Text Detection using Transformers](https://mlsanity.com/paper/2202.11221/) (2022)
- [BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network](https://mlsanity.com/paper/2104.04903/) (2021)
- [Spotlight Text Detector: Spotlight on Candidate Regions Like a Camera](https://mlsanity.com/paper/2409.16820/) (2024)
- [LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting](https://mlsanity.com/paper/2511.05818/) (2025)
- [PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer](https://mlsanity.com/paper/2308.15004/) (2023)
- [The Devil is in Fine-tuning and Long-tailed Problems:A New Benchmark for Scene Text Detection](https://mlsanity.com/paper/2505.15649/) (2025)
- [CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer](https://mlsanity.com/paper/2307.13310/) (2023)
- [Towards Unified Multi-granularity Text Detection with Interactive Attention](https://mlsanity.com/paper/2405.19765/) (2024)
- [On Exploring and Improving Robustness of Scene Text Detection Models](https://mlsanity.com/paper/2110.05700/) (2021)
- [Artistic-style text detector and a new Movie-Poster dataset](https://mlsanity.com/paper/2406.16307/) (2024)

## Abstract (arXiv)

Arbitrary-shaped text detection is a challenging task due to the complex geometric layouts of texts such as large aspect ratios, various scales, random rotations and curve shapes. Most state-of-the-art methods solve this problem from bottom-up perspectives, seeking to model a text instance of complex geometric layouts with simple local units (e.g., local boxes or pixels) and generate detections with heuristic post-processings. In this work, we propose an arbitrary-shaped text detection method, namely TextRay, which conducts top-down contour-based geometric modeling and geometric parameter learning within a single-shot anchor-free framework. The geometric modeling is carried out under polar system with a bidirectional mapping scheme between shape space and parameter space, encoding complex geometric layouts into unified representations. For effective learning of the representations, we design a central-weighted training strategy and a content loss which builds propagation paths between geometric encodings and visual content. TextRay outputs simple polygon detections at one pass with only one NMS post-processing. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed approach. The code is available at https://github.com/LianaWang/TextRay.