Scene Text Detection On Icdar 2017 Mlt 1

Metriken

F-Measure

Precision

Recall

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

				Paper Title
PMTD*	80.13%	84.42	76.25	Pyramid Mask Text Detector
Corner Localization (single-scale)	66.8%	83.8	55.6	Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation
SBD	79.47%	82.75	76.44	Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
FOTS MS	70.75%	81.86	62.3	FOTS: Fast Oriented Text Spotting with a Unified Network
CharNet H-88	75.77%	81.27	70.97	Convolutional Character Networks
FOTS	67.25%	80.95	57.51	FOTS: Fast Oriented Text Spotting with a Unified Network
CRAFT	-	80.6	68.2	Character Region Awareness for Text Detection
SPCNET	74.1%	80.6	68.6	Scene Text Detection with Supervised Pyramid Context Network
PAN	74.3%	80	69.8	Mask R-CNN with Pyramid Attention Network for Scene Text Detection
GNNets	74.54%	79.63	70.06	Geometry Normalization Networks for Accurate Scene Text Detection
CharNet R-50	73.42%	77.07	70.1	Convolutional Character Networks
PSENet-1s	72.45%	77.01	68.4	Shape Robust Text Detection with Progressive Scale Expansion Network
PSENet (ResNet-152)	72.13%	75.35	69.18	Shape Robust Text Detection with Progressive Scale Expansion Network
Corner Localization (multi-scale)	72.4%	74.3	70.6	Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

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Scene Text Detection On Icdar 2017 Mlt 1 | SOTA | HyperAI