# Mask R-CNN with Pyramid Attention Network for Scene Text Detection

arXiv: https://arxiv.org/abs/1811.09058 · HTML: https://mlsanity.com/paper/1811.09058/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Industrial Scene Text Detection with Refined Feature-attentive Network](https://mlsanity.com/paper/2110.12663/) (2021)
- [Transfer Learning for Scene Text Recognition in Indian Languages](https://mlsanity.com/paper/2201.03180/) (2022)
- [Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory](https://mlsanity.com/paper/2209.06952/) (2022)
- [Towards Boosting the Accuracy of Non-Latin Scene Text Recognition](https://mlsanity.com/paper/2201.03185/) (2022)
- [MENTOR: Multilingual tExt detectioN TOward leaRning by analogy](https://mlsanity.com/paper/2403.07286/) (2024)

## Abstract (arXiv)

In this paper, we present a new Mask R-CNN based text detection approach which can robustly detect multi-oriented and curved text from natural scene images in a unified manner. To enhance the feature representation ability of Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate that PAN can suppress false alarms caused by text-like backgrounds more effectively. Our proposed approach has achieved superior performance on both multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text detection benchmark tasks by only using single-scale and single-model testing.