Shape Robust Text Detection with Progressive Scale Expansion Network
Python 2.7
PyTorch v0.4.1+
pyclipper
Polygon2
OpenCV 3+ (for c++ version pse)
Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.
CUDA_VISIBLE_DEVICES=0,1,2,3 python train_ic15.py
CUDA_VISIBLE_DEVICES=0 python test_ic15.py --scale 1 --resume [path of model]
Eval script for ICDAR 2015 and SCUT-CTW1500 cd eval
sh eval_ic15.sh
sh eval_ctw1500.sh
Performance (new version paper)
Method
Extra Data
Precision (%)
Recall (%)
F-measure (%)
FPS (1080Ti)
Model
PSENet-1s (ResNet50)
-
81.49
79.68
80.57
1.6
baiduyun (extract code: rxti)
PSENet-1s (ResNet50)
pretrain on IC17 MLT
86.92
84.5
85.69
3.8
baiduyun (extract code: aieo)
PSENet-4s (ResNet50)
pretrain on IC17 MLT
86.1
83.77
84.92
3.8
baiduyun (extract code: aieo)
Method
Extra Data
Precision (%)
Recall (%)
F-measure (%)
FPS ((1080Ti)
Model
PSENet-1s (ResNet50)
-
80.57
75.55
78.0
3.9
baiduyun (extract code: ksv7)
PSENet-1s (ResNet50)
pretrain on IC17 MLT
84.84
79.73
82.2
3.9
baiduyun (extract code: z7ac)
PSENet-4s (ResNet50)
pretrain on IC17 MLT
82.09
77.84
79.9
8.4
baiduyun (extract code: z7ac)
Performance (old version paper on arxiv)
Method
Precision (%)
Recall (%)
F-measure (%)
PSENet-4s (ResNet152)
87.98
83.87
85.88
PSENet-2s (ResNet152)
89.30
85.22
87.21
PSENet-1s (ResNet152)
88.71
85.51
87.08
Method
Precision (%)
Recall (%)
F-measure (%)
PSENet-4s (ResNet152)
75.98
67.56
71.52
PSENet-2s (ResNet152)
76.97
68.35
72.40
PSENet-1s (ResNet152)
77.01
68.40
72.45
Method
Precision (%)
Recall (%)
F-measure (%)
PSENet-4s (ResNet152)
80.49
78.13
79.29
PSENet-2s (ResNet152)
81.95
79.30
80.60
PSENet-1s (ResNet152)
82.50
79.89
81.17
Method
Precision (%)
Recall (%)
F-measure (%)
PSENet-1s (ResNet152)
78.5
72.1
75.2
Figure 3: The results on ICDAR 2015, ICDAR 2017 MLT and SCUT-CTW1500
