From 30bc3c4396e69a7f2db48acb21291248cab91099 Mon Sep 17 00:00:00 2001
From: huangjun12 <12272008@bjtu.edu.cn>
Date: Thu, 28 Apr 2022 06:39:26 +0000
Subject: [PATCH] add sast ch doc
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doc/doc_en/algorithm_det_sast_en.md | 2 -
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+# SAST
+
+- [1. 算法简介](#1)
+- [2. 环境配置](#2)
+- [3. 模型训练、评估、预测](#3)
+ - [3.1 训练](#3-1)
+ - [3.2 评估](#3-2)
+ - [3.3 预测](#3-3)
+- [4. 推理部署](#4)
+ - [4.1 Python推理](#4-1)
+- [5. FAQ](#5)
+
+
+## 1. 算法简介
+
+论文信息：
+> [A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning](https://arxiv.org/abs/1908.05498)
+> Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming
+> ACM MM, 2019
+
+在ICDAR2015文本检测公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- | --- |
+|SAST|ResNet50_vd|[configs/det/det_r50_vd_sast_icdar15.yml](../../configs/det/det_r50_vd_sast_icdar15.yml)|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+
+
+在Total-text文本检测公开数据集上，算法复现效果如下：
+
+|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
+| --- | --- | --- | --- | --- | --- | --- |
+|SAST|ResNet50_vd|[configs/det/det_r50_vd_sast_totaltext.yml](../../configs/det/det_r50_vd_sast_totaltext.yml)|89.63%|78.44%|83.66%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
+
+
+
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+
+## 3. 模型训练、评估、预测
+
+请参考[文本检测训练教程](./detection.md)。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
+
+
+
+## 4. 推理部署
+
+
+### 4.1 Python推理
+首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar) )，可以使用如下命令进行转换：
+
+```shell
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast
+```
+
+SAST文本检测模型推理，可以执行如下命令：
+
+```shell
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast/"
+```
+
+可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
+
+
+
+**注意**：由于ICDAR2015数据集只有1000张训练图像，且主要针对英文场景，所以上述模型对中文文本图像检测效果会比较差。
+
+
+
+## 5. FAQ
+
+
+## 引用
+
+```bibtex
+@inproceedings{wang2019single,
+ title={A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning},
+ author={Wang, Pengfei and Zhang, Chengquan and Qi, Fei and Huang, Zuming and En, Mengyi and Han, Junyu and Liu, Jingtuo and Ding, Errui and Shi, Guangming},
+ booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
+ pages={1277--1285},
+ year={2019}
+}
+```
diff --git a/doc/doc_en/algorithm_det_sast_en.md b/doc/doc_en/algorithm_det_sast_en.md
index b302e329..e76ddd67 100644
--- a/doc/doc_en/algorithm_det_sast_en.md
+++ b/doc/doc_en/algorithm_det_sast_en.md
@@ -8,8 +8,6 @@
- [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)
- [5. FAQ](#5)
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