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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