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

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

22 Nov 2018arXiv:1811.09058archive 2025-07-28

Zhida Huang, Zhuoyao Zhong, Lei Sun, Qiang Huo

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.

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Tasks

Curved Text DetectionScene Text DetectionText Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Scene Text Detection ICDAR 2015 PAN F-Measure 85.9 #22 of 43 Archive leaderboard report
Scene Text Detection ICDAR 2015 PAN Precision 90.8 #22 of 43 Archive leaderboard report
Scene Text Detection ICDAR 2015 PAN Recall 81.5 #22 of 43 Archive leaderboard report
Scene Text Detection ICDAR 2017 MLT PAN F-Measure 74.3% #9 of 14 Archive leaderboard report
Scene Text Detection ICDAR 2017 MLT PAN Precision 80 #9 of 14 Archive leaderboard report
Scene Text Detection ICDAR 2017 MLT PAN Recall 69.8 #9 of 14 Archive leaderboard report
Scene Text Detection SCUT-CTW1500 PAN F-Measure 85 #6 of 17 Archive leaderboard report
Scene Text Detection SCUT-CTW1500 PAN FPS 65.2 #6 of 17 Archive leaderboard report
Scene Text Detection SCUT-CTW1500 PAN Precision 86.8 #6 of 17 Archive leaderboard report
Scene Text Detection SCUT-CTW1500 PAN Recall 83.2 #6 of 17 Archive leaderboard report

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Methods

ConvolutionMask R-CNNRPNRoIAlignSoftmax

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