# Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

arXiv: https://arxiv.org/abs/2107.12664 · HTML: https://mlsanity.com/paper/2107.12664/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 43 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Real-time Scene Text Detection with Differentiable Binarization](https://mlsanity.com/paper/1911.08947/) (2019)
- [Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://mlsanity.com/paper/1703.01086/) (2017)
- [Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes](https://mlsanity.com/paper/1908.08207/) (2019)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://mlsanity.com/paper/1803.09693/) (2018)
- [TextBoxes: A Fast Text Detector with a Single Deep Neural Network](https://mlsanity.com/paper/1611.06779/) (2016)
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://mlsanity.com/paper/1801.02765/) (2018)
- [TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes](https://mlsanity.com/paper/1807.01544/) (2018)
- [Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network](https://mlsanity.com/paper/1908.05900/) (2019)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [Fast Interactive Object Annotation with Curve-GCN](https://mlsanity.com/paper/1903.06874/) (2019)
- [Rotation-Sensitive Regression for Oriented Scene Text Detection](https://mlsanity.com/paper/1803.05265/) (2018)
- [Detecting Text in Natural Image with Connectionist Text Proposal Network](https://mlsanity.com/paper/1609.03605/) (2016)
- [Detecting Oriented Text in Natural Images by Linking Segments](https://mlsanity.com/paper/1703.06520/) (2017)
- [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2003.07493/) (2020)
- [ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection](https://mlsanity.com/paper/2004.04940/) (2020)
- [Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes](https://mlsanity.com/paper/1904.06535/) (2019)
- [All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting](https://mlsanity.com/paper/1911.09550/) (2019)
- [PixelLink: Detecting Scene Text via Instance Segmentation](https://mlsanity.com/paper/1801.01315/) (2018)
- [Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting](https://mlsanity.com/paper/2002.06820/) (2020)
- [TextField: Learning A Deep Direction Field for Irregular Scene Text Detection](https://mlsanity.com/paper/1812.01393/) (2018)
- [Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation](https://mlsanity.com/paper/1905.05980/) (2019)

## Built on by (most cited first, 30 of 47)

- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [Towards End-to-End Unified Scene Text Detection and Layout Analysis](https://mlsanity.com/paper/2203.15143/) (2022)
- [Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection](https://mlsanity.com/paper/2203.15221/) (2022)
- [DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer](https://mlsanity.com/paper/2207.04491/) (2022)
- [Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation](https://mlsanity.com/paper/2401.17904/) (2024)
- [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)
- [Kernel Proposal Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2203.06410/) (2022)
- [Arbitrary Shape Text Detection via Segmentation with Probability Maps](https://mlsanity.com/paper/2208.12419/) (2022)
- [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)
- [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)
- [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)
- [Unified Line and Paragraph Detection by Graph Convolutional Networks](https://mlsanity.com/paper/2203.09638/) (2022)
- [Graph Fusion Network for Multi-Oriented Object Detection](https://mlsanity.com/paper/2205.03562/) (2022)
- [CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection](https://mlsanity.com/paper/2212.02340/) (2022)
- [Text Detection Forgot About Document OCR](https://mlsanity.com/paper/2210.07903/) (2022)
- [Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification](https://mlsanity.com/paper/2112.12927/) (2021)
- [MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild](https://mlsanity.com/paper/2308.12817/) (2023)
- [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)
- [SD-GAN: Semantic Decomposition for Face Image Synthesis with Discrete Attribute](https://mlsanity.com/paper/2207.05300/) (2022)
- [PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer](https://mlsanity.com/paper/2308.15004/) (2023)
- [DeepEraser: Deep Iterative Context Mining for Generic Text Eraser](https://mlsanity.com/paper/2402.19108/) (2024)

## Abstract (arXiv)

Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing. Our method mainly consists of a boundary proposal model and an innovative adaptive boundary deformation model. The boundary proposal model constructed by multi-layer dilated convolutions is adopted to produce prior information (including classification map, distance field, and direction field) and coarse boundary proposals. The adaptive boundary deformation model is an encoder-decoder network, in which the encoder mainly consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network (RNN). It aims to perform boundary deformation in an iterative way for obtaining text instance shape guided by prior information from the boundary proposal model. In this way, our method can directly and efficiently generate accurate text boundaries without complex post-processing. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method.