# Multi-Oriented Text Detection with Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1604.04018 · HTML: https://mlsanity.com/paper/1604.04018/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2016.

## Built on by (most cited first, 30 of 36)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)
- [ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting](https://mlsanity.com/paper/2105.03620/) (2021)
- [Turning a CLIP Model into a Scene Text Detector](https://mlsanity.com/paper/2302.14338/) (2023)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [Primitive Representation Learning for Scene Text Recognition](https://mlsanity.com/paper/2105.04286/) (2021)
- [TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation](https://mlsanity.com/paper/2110.12826/) (2021)
- [Stroke-Based Scene Text Erasing Using Synthetic Data for Training](https://mlsanity.com/paper/2104.11493/) (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)
- [All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2106.12720/) (2021)
- [CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection](https://mlsanity.com/paper/2212.02340/) (2022)
- [A Scene-Text Synthesis Engine Achieved Through Learning from Decomposed Real-World Data](https://mlsanity.com/paper/2209.02397/) (2022)
- [TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection](https://mlsanity.com/paper/2104.11435/) (2021)
- [FoSp: Focus and Separation Network for Early Smoke Segmentation](https://mlsanity.com/paper/2306.04474/) (2023)
- [TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming](https://mlsanity.com/paper/2402.09811/) (2024)
- [Text Grouping Adapter: Adapting Pre-trained Text Detector for Layout Analysis](https://mlsanity.com/paper/2405.07481/) (2024)
- [The First Swahili Language Scene Text Detection and Recognition Dataset](https://mlsanity.com/paper/2405.11437/) (2024)
- [VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset](https://mlsanity.com/paper/2101.07542/) (2021)
- [Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection](https://mlsanity.com/paper/2103.09179/) (2021)
- [DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents](https://mlsanity.com/paper/2106.05542/) (2021)
- [Text detection and recognition based on a lensless imaging system](https://mlsanity.com/paper/2210.04244/) (2022)
- [EpiSAM: Character Segmentation in Challenging Stone Inscriptions](https://mlsanity.com/paper/2606.28859/) (2026)
- [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)
- [Bidirectional Regression for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2107.06129/) (2021)
- [Scene Text recognition with Full Normalization](https://mlsanity.com/paper/2109.01034/) (2021)
- [DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection](https://mlsanity.com/paper/2208.09878/) (2022)

## Abstract (arXiv)

In this paper, we propose a novel approach for text detec- tion in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Fi- nally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple ori- entations, languages and fonts. The proposed method con- sistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.