# An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches

arXiv: https://arxiv.org/abs/1804.09003 · HTML: https://mlsanity.com/paper/1804.09003/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting](https://mlsanity.com/paper/2105.03620/) (2021)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [Extending One-Stage Detection with Open-World Proposals](https://mlsanity.com/paper/2201.02302/) (2022)
- [Fast Region of Interest Proposals on Maritime UAVs](https://mlsanity.com/paper/2301.11650/) (2023)
- [CNN-based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction](https://mlsanity.com/paper/2108.06185/) (2021)
- [StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks](https://mlsanity.com/paper/2111.11718/) (2021)
- [MENTOR: Multilingual tExt detectioN TOward leaRning by analogy](https://mlsanity.com/paper/2403.07286/) (2024)

## Abstract (arXiv)

The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its IoU based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of complicated anchor design and achieve higher recall rate on large-scale COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN based two-stage text detection approach achieves state-of-the-art results on ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using single-scale and single-model (ResNet50) testing only.