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
- python 3.7.9, pytorch 1.4.0, and torchvision 0.5.0 (other versions may probably work, but didn't being tested)
- other libaries can be installed by
pip install -r requirements.txt
We provide code for using our pretrained model to recognize text images.
-
The pretrained model can be downloaded via Baidu net disk: download_link 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
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
Similar to train, one can modify Configs/testConf.py
and run python test.py
to evaluate a model.
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}
}