DocumentAIKit/doc/doc_en/algorithm_det_drrg_en.md

DRRG

• 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:

Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng CVPR, 2020

On the CTW1500 dataset, the text detection result is as follows:

Model	Backbone	Configuration	Precision	Recall	Hmean	Download
DRRG	ResNet50_vd	configs/det/det_r50_drrg_ctw.yml	89.92%	80.91%	85.18%	trained model

2. Environment

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

3. Model Training / Evaluation / Prediction

The above DRRG model is trained using the CTW1500 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

Since the model needs to be converted to Numpy data for many times in the forward, DRRG dynamic graph to static graph is not supported.

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@inproceedings{zhang2020deep,
  title={Deep relational reasoning graph network for arbitrary shape text detection},
  author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9699--9708},
  year={2020}
}