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Scene Text Detection on MLT 2017 (test)

87.3PrecisionOurs (RBox)
72.84476.59780.3584.103Apr 2, 2021Aug 11, 2021Dec 21, 2021May 1, 2022Sep 10, 2022Jan 19, 2023May 31, 2023

Evaluation results

MethodLinks
Ours (RBox)FPS=13.12022.0387.373.279.6—
DeepSolo++Backbone=Res-50, routing=true, pre-trained initialization index=#32023.0586.267.275.6—
Multiplexed TextSpotter2023.0585.462.972.4—
DeepSolo++Backbone=Res-50, routing=true, pre-trained initialization index=#12023.0585.365.273.9—
Raisi et al.2022.0384.863.272.4—
Xiao et al.2022.0384.272.878.1—
Lyu et al.FPS source=reported from CRAFT [1]2021.0483.855.666.85.7
Corner2022.0383.855.666.8—
BDN2021.0483.670.176.32.3
DBBackbone=deformable-ResNet-502021.0483.167.974.719
DBFPS=192022.0383.167.974.7—
MOSTInput Resolution=range (640, 1920)2021.04827276.710.1
MOSTFPS=10.12022.03827276.7—
FOTS2023.058157.567.3—
CRAFTFPS source=reported from CRAFT [1]2021.0480.668.273.98.6
CRAFTFPS=8.62022.0380.668.273.9—
GNNets2021.0479.670.174.52.3
LOMO2021.0478.860.668.5—
CharNet2023.0577.170.173.4—
baselineInput Resolution=range (640, 1920)2021.0475.96771.115.2
DRRG2021.04756167.3—
DRRG2022.03756167.3—
PSENet-1s2021.0473.868.270.9—
PSE-Net2022.0373.868.270.7—
SPCNet2021.0473.466.970—