TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Scene Text Detection	ICDAR 2015	PSENet-1s	F-Measure	85.7	# 19
Scene Text Detection	ICDAR 2015	PSENet-1s	Precision	86.9	# 23
Scene Text Detection	ICDAR 2015	PSENet-1s	Recall	84.5	# 15
Scene Text Detection	ICDAR 2017 MLT	PSENet (ResNet-152)	Precision	75.35	# 13
Scene Text Detection	ICDAR 2017 MLT	PSENet (ResNet-152)	Recall	69.18	# 8
Scene Text Detection	ICDAR 2017 MLT	PSENet (ResNet-152)	F-Measure	72.13%	# 11
Scene Text Detection	SCUT-CTW1500	PSENet-1s	F-Measure	82.2	# 5
Scene Text Detection	SCUT-CTW1500	PSENet-1s	Precision	84.8	# 5
Scene Text Detection	SCUT-CTW1500	PSENet-1s	Recall	79.7	# 7
Scene Text Detection	Total-Text	PSENet-4s	F-Measure	79.6%%	# 12
Scene Text Detection	Total-Text	PSENet-4s	Precision	84.5	# 7
Scene Text Detection	Total-Text	PSENet-4s	Recall	75.2	# 9

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Shape Robust Text Detection with Progressive Scale Expansion Network

CVPR 2019 • Wenhai Wang • Enze Xie • Xiang Li • Wenbo Hou • Tong Lu • Gang Yu • Shuai Shao

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications... (read more)

PDF Abstract CVPR 2019 PDF CVPR 2019 Abstract

Code

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whai362/PSENet

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WenmuZhou/PSENet.pytorch

417

weijiawu/Unconstrained-Text-Detection-with-Box-Supervisionand-Dynamic-Self-Training

li10141110/PSENet-tf2

Mael-zys/PSENet

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Tasks

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SCENE TEXT

SCENE TEXT DETECTION

Datasets

Total-Text

SCUT-CTW1500

ICDAR 2017

Results from the Paper

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Ranked #5 on Scene Text Detection on SCUT-CTW1500

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Scene Text Detection	ICDAR 2015	PSENet-1s	F-Measure	85.7	# 19	Compare
			Precision	86.9	# 23	Compare
			Recall	84.5	# 15	Compare
Scene Text Detection	ICDAR 2017 MLT	PSENet (ResNet-152)	Precision	75.35	# 13	Compare
			Recall	69.18	# 8	Compare
			F-Measure	72.13%	# 11	Compare
Scene Text Detection	SCUT-CTW1500	PSENet-1s	F-Measure	82.2	# 5	Compare
			Precision	84.8	# 5	Compare
			Recall	79.7	# 7	Compare
Scene Text Detection	Total-Text	PSENet-4s	F-Measure	79.6%%	# 12	Compare
			Precision	84.5	# 7	Compare
			Recall	75.2	# 9	Compare

Methods used in the Paper

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METHOD	TYPE
Average Pooling	Pooling Operations
Residual Connection	Skip Connections
ReLU	Activation Functions
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Bottleneck Residual Block	Skip Connection Blocks
Global Average Pooling	Pooling Operations
Residual Block	Skip Connection Blocks
Kaiming Initialization	Initialization
Max Pooling	Pooling Operations
Convolution	Convolutions
ResNet	Convolutional Neural Networks

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