TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Scene Text Detection	ICDAR 2013	Mask TextSpotter	F-Measure	91.7%	# 3
Scene Text Detection	ICDAR 2013	Mask TextSpotter	Precision	95	# 3
Scene Text Detection	ICDAR 2013	Mask TextSpotter	Recall	88.6	# 4
Scene Text Detection	ICDAR 2015	Mask TextSpotter	F-Measure	86	# 17
Scene Text Detection	ICDAR 2015	Mask TextSpotter	Precision	91.6	# 6
Scene Text Detection	ICDAR 2015	Mask TextSpotter	Recall	81	# 22
Scene Text Detection	Total-Text	Mask TextSpotter	F-Measure	61.3%	# 13
Scene Text Detection	Total-Text	Mask TextSpotter	Precision	69	# 11
Scene Text Detection	Total-Text	Mask TextSpotter	Recall	55	# 10

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Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

ECCV 2018 • Pengyuan Lyu • Minghui Liao • Cong Yao • Wenhao Wu • Xiang Bai

Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images... (read more)

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lvpengyuan/masktextspotter.caffe2

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SCENE TEXT

SCENE TEXT DETECTION

SEMANTIC SEGMENTATION

TEXT SPOTTING

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ICDAR 2013

Total-Text

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Ranked #3 on Scene Text Detection on ICDAR 2013

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Scene Text Detection	ICDAR 2013	Mask TextSpotter	F-Measure	91.7%	# 3	Compare
			Precision	95	# 3	Compare
			Recall	88.6	# 4	Compare
Scene Text Detection	ICDAR 2015	Mask TextSpotter	F-Measure	86	# 17	Compare
			Precision	91.6	# 6	Compare
			Recall	81	# 22	Compare
Scene Text Detection	Total-Text	Mask TextSpotter	F-Measure	61.3%	# 13	Compare
			Precision	69	# 11	Compare
			Recall	55	# 10	Compare

Methods used in the Paper

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METHOD	TYPE
Softmax	Output Functions
RoIAlign	RoI Feature Extractors
Convolution	Convolutions
Mask R-CNN	Instance Segmentation Models

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