# DRRG - [1. Introduction](#1) - [2. Environment](#2) - [3. Model Training / Evaluation / Prediction](#3) - [3.1 Training](#3-1) - [3.2 Evaluation](#3-2) - [3.3 Prediction](#3-3) - [4. Inference and Deployment](#4) - [4.1 Python Inference](#4-1) - [4.2 C++ Inference](#4-2) - [4.3 Serving](#4-3) - [4.4 More](#4-4) - [5. FAQ](#5) ## 1. Introduction Paper: > [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://arxiv.org/abs/2003.07493) > 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](../../configs/det/det_r50_drrg_ctw.yml)| 89.92%|80.91%|85.18%|[trained model](https://paddleocr.bj.bcebos.com/contribution/det_r50_drrg_ctw_train.tar)| ## 2. Environment Please prepare your environment referring to [prepare the environment](./environment_en.md) and [clone the repo](./clone_en.md). ## 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](./dataset/ocr_datasets_en.md). After the data download is complete, please refer to [Text Detection Training Tutorial](./detection_en.md) 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 ```bibtex @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} } ```