Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Requirements

• pytorch 1.1+
• torchvision 0.3+
• pyclipper
• opencv3
• gcc 4.9+

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PAN_resnet18_FPEM_FFM and PAN_resnet18_FPEM_FFM on icdar2015：

the updated model(resnet18:78.8,shufflenetv2: 72.4,lr:le-3) is not the best model

google drive

Data Preparation

train: prepare a text in the following format, use '\t' as a separator

/path/to/img.jpg path/to/label.txt
...

val: use a folder

img/ store img
gt/ store gt file

Train

1. config the train_data_path,val_data_pathin config.json
2. use following script to run

python3 train.py

Test

eval.py is used to test model on test dataset

1. config model_path, img_path, gt_path, save_path in eval.py
2. use following script to test

python3 eval.py

Predict

predict.py is used to inference on single image

1. config model_path, img_path, in predict.py
2. use following script to predict

python3 predict.py

The project is still under development.

Performance

ICDAR 2015

only train on ICDAR2015 dataset

examples

todo

• MobileNet backbone

• ShuffleNet backbone

reference

1. https://arxiv.org/pdf/1908.05900.pdf
2. https://github.com/WenmuZhou/PSENet.pytorch

If this repository helps you，please star it. Thanks.