algorithm_det_psenet_en.md

PSENet

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ

1. Introduction

Paper:

Shape robust text detection with progressive scale expansion network Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai CVPR, 2019

On the ICDAR2015 dataset, the text detection result is as follows:

Model	Backbone	Configuration	Precision	Recall	Hmean	Download
PSE	ResNet50_vd	configs/det/det_r50_vd_pse.yml	85.81%	79.53%	82.55%	trained model
PSE	MobileNetV3	configs/det/det_mv3_pse.yml	82.20%	70.48%	75.89%	trained model

2. Environment

Please prepare your environment referring to prepare the environment and clone the repo.

3. Model Training / Evaluation / Prediction

The above PSE model is trained using the ICDAR2015 text detection public dataset. For the download of the dataset, please refer to ocr_datasets.

After the data download is complete, please refer to Text Detection Training Tutorial for training. PaddleOCR has modularized the code structure, so that you only need to replace the configuration file to train different detection models.

4. Inference and Deployment

4.1 Python Inference

First, convert the model saved in the PSE text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as example (model download link), you can use the following command to convert:

python3 tools/export_model.py -c configs/det/det_r50_vd_pse.yml -o Global.pretrained_model=./det_r50_vd_pse_v2.0_train/best_accuracy  Global.save_inference_dir=./inference/det_pse

PSE text detection model inference, to perform non-curved text detection, you can run the following commands:

python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=quad

The visualized text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

If you want to perform curved text detection, you can execute the following command:

python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_pse/" --det_algorithm="PSE" --det_pse_box_type=poly

The visualized text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

Note: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese or curved text images.

4.2 C++ Inference

Since the post-processing is not written in CPP, the PSE text detection model does not support CPP inference.

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@inproceedings{wang2019shape,
  title={Shape robust text detection with progressive scale expansion network},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9336--9345},
  year={2019}
}