/PSENet

Primary LanguageC++Apache License 2.0Apache-2.0

Shape Robust Text Detection with Progressive Scale Expansion Network

Requirements

  • Python 2.7
  • PyTorch v0.4.1+
  • pyclipper
  • Polygon2
  • OpenCV 3.4 (for c++ version pse)
  • opencv-python 3.4

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]

Eval script for ICDAR 2015 and SCUT-CTW1500

cd eval
sh eval_ic15.sh
sh eval_ctw1500.sh

Performance (new version paper)

ICDAR 2015

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); OneDrive
PSENet-1s (ResNet50) pretrain on IC17 MLT 86.92 84.5 85.69 3.8 baiduyun(extract code: aieo); OneDrive
PSENet-4s (ResNet50) pretrain on IC17 MLT 86.1 83.77 84.92 3.8 baiduyun(extract code: aieo); OneDrive

SCUT-CTW1500

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); OneDrive
PSENet-1s (ResNet50) pretrain on IC17 MLT 84.84 79.73 82.2 3.9 baiduyun(extract code: z7ac); OneDrive
PSENet-4s (ResNet50) pretrain on IC17 MLT 82.09 77.84 79.9 8.4 baiduyun(extract code: z7ac); OneDrive

Performance (old version paper)

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

Paper Link

[new version paper] https://arxiv.org/abs/1903.12473

[old version paper] https://arxiv.org/abs/1806.02559

Other Implements

[tensorflow version (thanks @liuheng92)] https://github.com/liuheng92/tensorflow_PSENet