Primitive Representation Learning Network (PREN)

This repository contains the code for our paper accepted by CVPR 2021

Primitive Representation Learning for Scene Text Recognition

Ruijie Yan, Liangrui Peng, Shanyu Xiao, Gang Yao

For now we only provide code for PREN.

Requirements

python 3.7.9, pytorch 1.4.0, and torchvision 0.5.0
other libraries can be installed by
```
pip install -r requirements.txt
```

Recognition with pretrained model

We provide code for using our pretrained model to recognize text images.

The pretrained model can be downloaded via Google drive or Baidu net disk (key: 2txt)
After downloading the pretrained model (pren.pth), put it in the "models" folder.
To recognize three samples in the "samples" folder, just run
```
python recog.py
```

The results would be

[Info] Load model from ./models/pren.pth
samples/001.jpg: ronaldo
samples/002.png: leaves
samples/003.jpg: salmon

Training

Two simple steps to train your own model:

Modify training configurations in Configs/trainConf.py
Run python train.py

To run the training code, please modify image_dir and train_list to your own training data.

image_dir is the path of training data root.

train_list is the path of a text file containing image paths (relative to image_dir) and corresponding labels.

For example, image_dir could be './samples', and train_list could be a text file with the following content

001.jpg RONALDO
002.png LEAVES
003.jpg SALMON

Evaluation

Similar to train, one can modify Configs/testConf.py and run python test.py to evaluate a model.

Acknowledgement

The code of EfficientNet is modified from EfficientNet-PyTorch, where we output multi-scale feature maps.

Citation

If you find this project helpful for your research, please cite our paper

@inproceedings{yan2021primitive,
  author    = {Yan, Ruijie and
               Peng, Liangrui and
               Xiao, Shanyu and
               Yao, Gang},
  title     = {Primitive Representation Learning for Scene Text Recognition},
  booktitle = {CVPR},
  year      = {2021},
  pages     = {284-293}
}

RuijieJ / pren

readme