# Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

arXiv: https://arxiv.org/abs/2003.07493 · HTML: https://mlsanity.com/paper/2003.07493/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 30 of 77)

- [ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting](https://mlsanity.com/paper/2105.03620/) (2021)
- [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2104.10442/) (2021)
- [Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection](https://mlsanity.com/paper/2203.15221/) (2022)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network](https://mlsanity.com/paper/2104.05458/) (2021)
- [Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2107.12664/) (2021)
- [Turning a CLIP Model into a Scene Text Detector](https://mlsanity.com/paper/2302.14338/) (2023)
- [MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding](https://mlsanity.com/paper/2108.06543/) (2021)
- [MOST: A Multi-Oriented Scene Text Detector with Localization Refinement](https://mlsanity.com/paper/2104.01070/) (2021)
- [ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer](https://mlsanity.com/paper/2308.10147/) (2023)
- [Arbitrary Shape Text Detection via Boundary Transformer](https://mlsanity.com/paper/2205.05320/) (2022)
- [Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting](https://mlsanity.com/paper/2203.03911/) (2022)
- [TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation](https://mlsanity.com/paper/2110.12826/) (2021)
- [Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition](https://mlsanity.com/paper/2112.12916/) (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)
- [Post-OCR Paragraph Recognition by Graph Convolutional Networks](https://mlsanity.com/paper/2101.12741/) (2021)
- [Arbitrary Shape Text Detection via Segmentation with Probability Maps](https://mlsanity.com/paper/2208.12419/) (2022)
- [I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection](https://mlsanity.com/paper/2108.01343/) (2021)
- [CentripetalText: An Efficient Text Instance Representation for Scene Text Detection](https://mlsanity.com/paper/2107.05945/) (2021)
- [TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask](https://mlsanity.com/paper/2206.13381/) (2022)
- [COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts](https://mlsanity.com/paper/2207.04675/) (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)
- [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://mlsanity.com/paper/2111.02394/) (2021)
- [Recurrent Generic Contour-based Instance Segmentation with Progressive Learning](https://mlsanity.com/paper/2301.08898/) (2023)
- [Turning a CLIP Model into a Scene Text Spotter](https://mlsanity.com/paper/2308.10408/) (2023)
- [Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling](https://mlsanity.com/paper/2401.03637/) (2024)
- [Seeing Text in the Dark: Algorithm and Benchmark](https://mlsanity.com/paper/2404.08965/) (2024)

## Abstract (arXiv)

Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reasoning network via Graph Convolutional Network (GCN), making our network end-to-end trainable. To be concrete, every text instance will be divided into a series of small rectangular components, and the geometry attributes (e.g., height, width, and orientation) of the small components will be estimated by our text proposal model. Given the geometry attributes, the local graph construction model can roughly establish linkages between different text components. For further reasoning and deducing the likelihood of linkages between the component and its neighbors, we adopt a graph-based network to perform deep relational reasoning on local graphs. Experiments on public available datasets demonstrate the state-of-the-art performance of our method.