# Industrial Scene Text Detection with Refined Feature-attentive Network

arXiv: https://arxiv.org/abs/2110.12663 · HTML: https://mlsanity.com/paper/2110.12663/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 77 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (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)
- [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://mlsanity.com/paper/1908.05612/) (2019)
- [Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://mlsanity.com/paper/1703.01086/) (2017)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019](https://mlsanity.com/paper/1907.00945/) (2019)
- [Fully Convolutional Instance-aware Semantic Segmentation](https://mlsanity.com/paper/1611.07709/) (2016)
- [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)
- [FOTS: Fast Oriented Text Spotting with a Unified Network](https://mlsanity.com/paper/1801.01671/) (2018)
- [Rotation-Sensitive Regression for Oriented Scene Text Detection](https://mlsanity.com/paper/1803.05265/) (2018)
- [Detecting Oriented Text in Natural Images by Linking Segments](https://mlsanity.com/paper/1703.06520/) (2017)
- [Fourier Contour Embedding for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2104.10442/) (2021)
- [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)
- [Convolutional Character Networks](https://mlsanity.com/paper/1910.07954/) (2019)
- [PixelLink: Detecting Scene Text via Instance Segmentation](https://mlsanity.com/paper/1801.01315/) (2018)
- [Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation](https://mlsanity.com/paper/1905.05980/) (2019)

## Built on by (most cited first, 16 of 16)

- [Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer](https://mlsanity.com/paper/2311.13120/) (2023)
- [Self-supervised Character-to-Character Distillation for Text Recognition](https://mlsanity.com/paper/2211.00288/) (2022)
- [Self-supervised Implicit Glyph Attention for Text Recognition](https://mlsanity.com/paper/2203.03382/) (2022)
- [Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding](https://mlsanity.com/paper/2503.14140/) (2025)
- [PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer](https://mlsanity.com/paper/2407.07764/) (2024)
- [A Token-level Text Image Foundation Model for Document Understanding](https://mlsanity.com/paper/2503.02304/) (2025)
- [When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding](https://mlsanity.com/paper/2506.05551/) (2025)
- [Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors](https://mlsanity.com/paper/2312.05286/) (2023)
- [Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review](https://mlsanity.com/paper/2502.16586/) (2025)
- [Real-Time Text Detection with Similar Mask in Traffic, Industrial, and Natural Scenes](https://mlsanity.com/paper/2411.02794/) (2024)
- [Edge Approximation Text Detector](https://mlsanity.com/paper/2504.04001/) (2025)
- [CodePercept: Code-Grounded Visual STEM Perception for MLLMs](https://mlsanity.com/paper/2603.10757/) (2026)
- [CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer](https://mlsanity.com/paper/2307.13310/) (2023)
- [LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement](https://mlsanity.com/paper/2606.23539/) (2026)
- [Text-Pass Filter: An Efficient Scene Text Detector](https://mlsanity.com/paper/2601.18098/) (2026)
- [TextDS: Parameter-Efficient Representation Alignment for Scene Text Detection under Distribution Shifts](https://mlsanity.com/paper/2606.28077/) (2026)

## Abstract (arXiv)

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods locate low-contrast text areas inaccurately. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive refinement network is developed by the attention map to rectify the location deviation of candidate boxes. In addition, a re-scoring mechanism is designed to select text boxes with the best rectified location. Moreover, we construct two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance.