diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index 66295b25252e3906b4d3e6ffb30b135f0c6bdf6c..fbc0b9e70322fc23363c3368883d100f8ad68ac5 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -1,22 +1,25 @@
-# 目录
-- [1. 文字检测](#1-----)
- * [1.1 数据准备](#11-----)
- * [1.2 下载预训练模型](#12--------)
- * [1.3 启动训练](#13-----)
- * [1.4 断点训练](#14-----)
- * [1.5 更换Backbone 训练](#15---backbone---)
- * [1.6 指标评估](#16-----)
- * [1.7 测试检测效果](#17-------)
- * [1.8 转inference模型测试](#18--inference----)
-- [2. FAQ](#2-faq)
-
-
-
-# 1. 文字检测
+# 文字检测
本节以icdar2015数据集为例，介绍PaddleOCR中检测模型训练、评估、测试的使用方式。
+- [1. 准备数据和模型](#1--------)
+ * [1.1 数据准备](#11-----)
+ * [1.2 下载预训练模型](#12--------)
+- [2. 开始训练](#2-----)
+ * [2.1 启动训练](#21-----)
+ * [2.2 断点训练](#22-----)
+ * [2.3 更换Backbone 训练](#23---backbone---)
+- [3. 模型评估与预测](#3--------)
+ * [3.1 指标评估](#31-----)
+ * [3.2 测试检测效果](#32-------)
+- [4. 模型导出与预测](#4--------)
+ * [4.1 转inference模型测试](#41--inference----)
+- [5. FAQ](#5-faq)
+
+
+# 1. 准备数据和模型
+
## 1.1 数据准备
@@ -83,8 +86,11 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
```
-
-## 1.3 启动训练
+
+# 2. 开始训练
+
+
+## 2.1 启动训练
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
@@ -106,8 +112,8 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/
python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
```
-
-## 1.4 断点训练
+
+## 2.2 断点训练
如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
```shell
@@ -116,8 +122,8 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
-
-## 1.5 更换Backbone 训练
+
+## 2.3 更换Backbone 训练
PaddleOCR将网络划分为四部分，分别在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones->
necks->heads)依次通过这四个部分。
@@ -164,8 +170,11 @@ args1: args1
**注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
-
-## 1.6 指标评估
+
+# 3. 模型评估与预测
+
+
+## 3.1 指标评估
PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean（F-Score）。
@@ -177,8 +186,8 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
* 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
-
-## 1.7 测试检测效果
+
+## 3.2 测试检测效果
测试单张图像的检测效果
```shell
@@ -195,8 +204,11 @@ python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
```
-
-## 1.8 转inference模型测试
+
+# 4. 模型导出与预测
+
+
+## 4.1 转inference模型测试
inference 模型（`paddle.jit.save`保存的模型）
一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。
@@ -218,8 +230,8 @@ python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./outpu
python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
```
-
-# 2. FAQ
+
+# 5. FAQ
Q1: 训练模型转inference 模型之后预测效果不一致？
**A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。以det_mv3_db.yml配置文件训练的模型为例，训练模型、inference模型预测结果不一致问题解决方式如下：
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index d3f6f3da102d06c53e4e179a0bd89670536e1af7..bdfde6720df82f4773459792e031b99fd030bdb9 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -1,21 +1,22 @@
-# CONTENT
+# TEXT DETECTION
-- [Paste Your Document In Here](#paste-your-document-in-here)
-- [1. TEXT DETECTION](#1-text-detection)
+This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
+
+- [1. DATA AND WEIGHTS PREPARATIO](#1-data-and-weights-preparatio)
* [1.1 DATA PREPARATION](#11-data-preparation)
* [1.2 DOWNLOAD PRETRAINED MODEL](#12-download-pretrained-model)
- * [1.3 START TRAINING](#13-start-training)
- * [1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING](#14-load-trained-model-and-continue-training)
- * [1.5 TRAINING WITH NEW BACKBONE](#15-training-with-new-backbone)
- * [1.6 EVALUATION](#16-evaluation)
- * [1.7 TEST](#17-test)
- * [1.8 INFERENCE MODEL PREDICTION](#18-inference-model-prediction)
+- [2. TRAINING](#2-training)
+ * [2.1 START TRAINING](#21-start-training)
+ * [2.2 LOAD TRAINED MODEL AND CONTINUE TRAINING](#22-load-trained-model-and-continue-training)
+ * [2.3 TRAINING WITH NEW BACKBONE](#23-training-with-new-backbone)
+- [3. EVALUATION AND TEST](#3-evaluation-and-test)
+ * [3.1 EVALUATION](#31-evaluation)
+ * [3.2 TEST](#32-test)
+- [4. INFERENCE](#4-inference)
+ * [4.1 INFERENCE MODEL PREDICTION](#41-inference-model-prediction)
- [2. FAQ](#2-faq)
-
-# 1. TEXT DETECTION
-
-This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
+# 1 DATA AND WEIGHTS PREPARATIO
## 1.1 DATA PREPARATION
@@ -75,7 +76,10 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
```
-## 1.3 START TRAINING
+# 2. TRAINING
+
+## 2.1 START TRAINING
+
*If CPU version installed, please set the parameter `use_gpu` to `false` in the configuration.*
```shell
python3 tools/train.py -c configs/det/det_mv3_db.yml \
@@ -98,7 +102,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs
```
-## 1.4 LOAD TRAINED MODEL AND CONTINUE TRAINING
+## 2.2 LOAD TRAINED MODEL AND CONTINUE TRAINING
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
For example:
@@ -109,7 +113,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
-## 1.5 TRAINING WITH NEW BACKBONE
+## 2.3 TRAINING WITH NEW BACKBONE
The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
necks->heads).
@@ -159,7 +163,9 @@ After adding the four-part modules of the network, you only need to configure th
**NOTE**: More details about replace Backbone and other mudule can be found in [doc](add_new_algorithm_en.md).
-## 1.6 EVALUATION
+# 3. EVALUATION AND TEST
+
+## 3.1 EVALUATION
PaddleOCR calculates three indicators for evaluating performance of OCR detection task: Precision, Recall, and Hmean(F-Score).
@@ -174,7 +180,7 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="{pat
* Note: `box_thresh` and `unclip_ratio` are parameters required for DB post-processing, and not need to be set when evaluating the EAST and SAST model.
-## 1.7 TEST
+## 3.2 TEST
Test the detection result on a single image:
```shell
@@ -192,7 +198,9 @@ Test the detection result on all images in the folder:
python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
```
-## 1.8 INFERENCE MODEL PREDICTION
+# 4. INFERENCE
+
+## 4.1 INFERENCE MODEL PREDICTION
The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.