# Omnidirectional Scene Text Detection with Sequential-free Box Discretization

arXiv: https://arxiv.org/abs/1906.02371 · HTML: https://mlsanity.com/paper/1906.02371/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 26 of 26)

- [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://mlsanity.com/paper/2101.11952/) (2021)
- [Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval](https://mlsanity.com/paper/2204.09868/) (2022)
- [Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection](https://mlsanity.com/paper/2203.15221/) (2022)
- [What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels](https://mlsanity.com/paper/2103.04400/) (2021)
- [Revisiting Scene Text Recognition: A Data Perspective](https://mlsanity.com/paper/2307.08723/) (2023)
- [Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization](https://mlsanity.com/paper/2209.10839/) (2022)
- [MOST: A Multi-Oriented Scene Text Detector with Localization Refinement](https://mlsanity.com/paper/2104.01070/) (2021)
- [Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss](https://mlsanity.com/paper/2103.11636/) (2021)
- [Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering](https://mlsanity.com/paper/2209.10326/) (2022)
- [Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection](https://mlsanity.com/paper/2109.03426/) (2021)
- [Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature](https://mlsanity.com/paper/2305.11033/) (2023)
- [G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection](https://mlsanity.com/paper/2205.11796/) (2022)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning](https://mlsanity.com/paper/2308.07202/) (2023)
- [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://mlsanity.com/paper/2111.02394/) (2021)
- [Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences](https://mlsanity.com/paper/2106.10681/) (2021)
- [CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection](https://mlsanity.com/paper/2212.02340/) (2022)
- [Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA](https://mlsanity.com/paper/2310.09147/) (2023)
- [Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering](https://mlsanity.com/paper/2203.12929/) (2022)
- [OSKDet: Towards Orientation-sensitive Keypoint Localization for Rotated Object Detection](https://mlsanity.com/paper/2104.08697/) (2021)
- [StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks](https://mlsanity.com/paper/2111.11718/) (2021)
- [MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images](https://mlsanity.com/paper/2206.15109/) (2022)
- [Reading Chinese in Natural Scenes with a Bag-of-Radicals Prior](https://mlsanity.com/paper/2210.02576/) (2022)
- [DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games](https://mlsanity.com/paper/2211.05429/) (2022)
- [Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using Diffusion Models](https://mlsanity.com/paper/2311.16555/) (2023)
- [Text region detection in historical astronomical diagrams](https://mlsanity.com/paper/2606.15886/) (2026)

## Abstract (arXiv)

Scene text in the wild is commonly presented with high variant characteristics. Using quadrilateral bounding box to localize the text instance is nearly indispensable for detection methods. However, recent researches reveal that introducing quadrilateral bounding box for scene text detection will bring a label confusion issue which is easily overlooked, and this issue may significantly undermine the detection performance. To address this issue, in this paper, we propose a novel method called Sequential-free Box Discretization (SBD) by discretizing the bounding box into key edges (KE) which can further derive more effective methods to improve detection performance. Experiments showed that the proposed method can outperform state-of-the-art methods in many popular scene text benchmarks, including ICDAR 2015, MLT, and MSRA-TD500. Ablation study also showed that simply integrating the SBD into Mask R-CNN framework, the detection performance can be substantially improved. Furthermore, an experiment on the general object dataset HRSC2016 (multi-oriented ships) showed that our method can outperform recent state-of-the-art methods by a large margin, demonstrating its powerful generalization ability. Source code: https://github.com/Yuliang-Liu/Box_Discretization_Network.