# Single Shot Text Detector with Regional Attention

arXiv: https://arxiv.org/abs/1709.00138 · HTML: https://mlsanity.com/paper/1709.00138/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2017.

## Built on by (most cited first, 30 of 31)

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)
- [PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text](https://mlsanity.com/paper/2105.00405/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Turning a CLIP Model into a Scene Text Detector](https://mlsanity.com/paper/2302.14338/) (2023)
- [Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss](https://mlsanity.com/paper/2103.11636/) (2021)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (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)
- [Industrial Scene Text Detection with Refined Feature-attentive Network](https://mlsanity.com/paper/2110.12663/) (2021)
- [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)
- [Turning a CLIP Model into a Scene Text Spotter](https://mlsanity.com/paper/2308.10408/) (2023)
- [Zoom Text Detector](https://mlsanity.com/paper/2209.03014/) (2022)
- [TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2207.06744/) (2022)
- [Detection and Rectification of Arbitrary Shaped Scene Texts by using Text Keypoints and Links](https://mlsanity.com/paper/2103.00785/) (2021)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Real-Time Text Detection with Similar Mask in Traffic, Industrial, and Natural Scenes](https://mlsanity.com/paper/2411.02794/) (2024)
- [HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System](https://mlsanity.com/paper/2206.02628/) (2022)
- [DExT: Detector Explanation Toolkit](https://mlsanity.com/paper/2212.11409/) (2022)
- [Sanity Checks for Saliency Methods Explaining Object Detectors](https://mlsanity.com/paper/2306.02424/) (2023)
- [StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks](https://mlsanity.com/paper/2111.11718/) (2021)
- [Arbitrary-Shaped Text Detection withAdaptive Text Region Representation](https://mlsanity.com/paper/2104.00297/) (2021)
- [MT: Multi-Perspective Feature Learning Network for Scene Text Detection](https://mlsanity.com/paper/2105.05455/) (2021)
- [Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition](https://mlsanity.com/paper/2106.05611/) (2021)
- [Scene Text recognition with Full Normalization](https://mlsanity.com/paper/2109.01034/) (2021)
- [An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text](https://mlsanity.com/paper/2304.08670/) (2023)
- [Research on Multilingual Natural Scene Text Detection Algorithm](https://mlsanity.com/paper/2312.11153/) (2023)
- [Text Region Multiple Information Perception Network for Scene Text Detection](https://mlsanity.com/paper/2401.10017/) (2024)
- [Focus Entirety and Perceive Environment for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2409.16827/) (2024)
- [Invizo: Arabic Handwritten Document Optical Character Recognition Solution](https://mlsanity.com/paper/2502.05277/) (2025)

## Abstract (arXiv)

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN- based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allow- ing the detector to work reliably on multi-scale and multi- orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://sstd.whuang.org/.