Shape Robust Text Detection with Progressive Scale Expansion Network

Requirements

• python 2.7
• PyTorch v0.4.1+
• pyclipper
• Polygon2
• OpenCV 3+ (for c++ version pse)

Todo

• CTW1500 train and test

Introduction

Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.

Training

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_ic15.py

Testing

CUDA_VISIBLE_DEVICES=0 python test_ic15.py --scale 1 --resume [path of model]

Performance (new version paper)

ICDAR 2015

Method	Extra Data	Precision (%)	Recall (%)	F-measure (%)	Model
PSENet-1s (ResNet50)	-	81.49	79.68	80.57	todo
PSENet-1s (ResNet50)	pretrain on IC17 MLT	86.92	84.5	85.69	todo
PSENet-4s (ResNet50)	pretrain on IC17 MLT	86.1	83.77	84.92	todo

Performance (old version paper on arxiv)

ICDAR 2015 (training with ICDAR 2017 MLT)

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

ICDAR 2017 MLT

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

SCUT-CTW1500

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

ICPR MTWI 2018 Challenge 2

Method	Precision (%)	Recall (%)	F-measure (%)
PSENet-1s (ResNet152)	78.5	72.1	75.2

Results

Figure 3: The results on ICDAR 2015, ICDAR 2017 MLT and SCUT-CTW1500