Merge pull request #7279 from an1018/dyg/update_doc

update doc

ppstructure/docs/models_list.md

@@ -10,13 +10,17 @@
 <a name="1"></a>
 ## 1. 版面分析模型
 
-|模型名称|模型简介|下载地址|label_map|
-| --- | --- | --- | --- |
-| ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [训练模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) |{0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}|
-| ppyolov2_r50vd_dcn_365e_tableBank_word | TableBank Word 数据集训练的版面分析模型，只能检测表格 | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | {0:"Table"}|
-| ppyolov2_r50vd_dcn_365e_tableBank_latex | TableBank Latex 数据集训练的版面分析模型，只能检测表格 | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | {0:"Table"}|
+|模型名称|模型简介|推理模型大小|下载地址|dict path|
+| --- | --- | --- | --- | --- |
+| picodet_lcnet_x1_0_fgd_layout | 基于PicoDet LCNet_x1_0和FGD蒸馏在PubLayNet 数据集训练的英文版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
+| ppyolov2_r50vd_dcn_365e_publaynet | 基于PP-YOLOv2在PubLayNet数据集上训练的英文版面分析模型 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [训练模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | 同上 |
+| picodet_lcnet_x1_0_fgd_layout_cdla | CDLA数据集训练的中文版面分析模型，可以划分为**表格、图片、图片标题、表格、表格标题、页眉、脚本、引用、公式**10类区域 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
+| picodet_lcnet_x1_0_fgd_layout_table | 表格数据集训练的版面分析模型，支持中英文文档表格区域的检测 | 9.7M | [推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
+| ppyolov2_r50vd_dcn_365e_tableBank_word | 基于PP-YOLOv2在TableBank Word 数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | 同上 |
+| ppyolov2_r50vd_dcn_365e_tableBank_latex | 基于PP-YOLOv2在TableBank Latex数据集训练的版面分析模型，支持英文文档表格区域的检测 | 221M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | 同上 |
 
 <a name="2"></a>
+
 ## 2. OCR和表格识别模型
 
 <a name="21"></a>

ppstructure/docs/models_list_en.md

@@ -6,15 +6,18 @@
   - [2.2 Table Recognition](#22-table-recognition)
 - [3. KIE](#3-kie)
 
-
 <a name="1"></a>
+
 ## 1. Layout Analysis
 
-|model name| description                                                                                                                                             |download|label_map|
-| --- |---------------------------------------------------------------------------------------------------------------------------------------------------------| --- | --- |
-| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet dataset, the model can recognition 5 types of areas such as **text, title, table, picture and list** | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) |{0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}|
-| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset, the model can only detect tables                                                       | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | {0:"Table"}|
-| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset, the model can only detect tables                                                      | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | {0:"Table"}|
+|model name| description                                                                                                                                             | inference model size                                                                                                                         |download|dict path|
+| --- |---------------------------------------------------------------------------------------------------------------------------------------------------------| --- | --- | --- |
+| picodet_lcnet_x1_0_fgd_layout | The layout analysis English model trained on the PubLayNet dataset based on PicoDet LCNet_x1_0 and FGD . the model can recognition 5 types of areas such as **Text, Title, Table, Picture and List** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
+| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel]](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | sme as above |
+| picodet_lcnet_x1_0_fgd_layout_cdla | The layout analysis Chinese model trained on the CDLA dataset, the model can recognition 10 types of areas such as **Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
+| picodet_lcnet_x1_0_fgd_layout_table | The layout analysis model trained on the table dataset, the model can detect tables in Chinese and English documents                     | 9.7M                                                  | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
+| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221M | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | same as above |
+| ppyolov2_r50vd_dcn_365e_tableBank_latex | The layout analysis model trained on the TableBank Latex dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221M                 | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | same as above |
 
 <a name="2"></a>
 ## 2. OCR and Table Recognition

ppstructure/docs/quickstart.md

@@ -8,15 +8,21 @@
     - [2.1.3 版面分析](#213-版面分析)
     - [2.1.4 表格识别](#214-表格识别)
     - [2.1.5 关键信息抽取](#215-关键信息抽取)
+    - [2.1.6 版面恢复](#216-版面恢复)
   - [2.2 代码使用](#22-代码使用)
-    - [2.2.1 图像方向分类版面分析表格识别](#221-图像方向分类版面分析表格识别)
+
+    - [2.2.1 图像方向+分类版面分析+表格识别](#221-图像方向分类版面分析表格识别)
     - [2.2.2 版面分析+表格识别](#222-版面分析表格识别)
     - [2.2.3 版面分析](#223-版面分析)
     - [2.2.4 表格识别](#224-表格识别)
+
     - [2.2.5 关键信息抽取](#225-关键信息抽取)
+    - [2.2.6 版面恢复](#226-版面恢复)
+
   - [2.3 返回结果说明](#23-返回结果说明)
     - [2.3.1 版面分析+表格识别](#231-版面分析表格识别)
     - [2.3.2 关键信息抽取](#232-关键信息抽取)
+
   - [2.4 参数说明](#24-参数说明)
 
 
@@ -24,11 +30,12 @@
 ## 1. 安装依赖包
 
 ```bash
-# 安装 paddleocr，推荐使用2.5+版本
-pip3 install "paddleocr>=2.5"
+# 安装 paddleocr，推荐使用2.6版本
+pip3 install "paddleocr>=2.6"
 # 安装 关键信息抽取 依赖包（如不需要KIE功能，可跳过）
 pip install -r kie/requirements.txt
-
+# 安装 图像方向分类依赖包paddleclas（如不需要图像方向分类功能，可跳过）
+pip3 install paddleclas
 ```
 
 <a name="2"></a>
@@ -62,15 +69,24 @@ paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/table.jpg --type=structur
 ```
 
 <a name="215"></a>
-#### 2.1.5 关键信息抽取
 
+#### 2.1.5 关键信息抽取
 请参考：[关键信息抽取教程](../kie/README_ch.md)。
 
+<a name="216"></a>
+
+#### 2.1.6 版面恢复
+
+```bash
+paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure --recovery=true
+```
+
 <a name="22"></a>
+
 ### 2.2 代码使用
 
 <a name="221"></a>
-#### 2.2.1 图像方向分类版面分析表格识别
+#### 2.2.1 图像方向分类+版面分析+表格识别
 
 ```python
 import os
@@ -149,6 +165,7 @@ for line in result:
 ```
 
 <a name="224"></a>
+
 #### 2.2.4 表格识别
 
 ```python
@@ -174,6 +191,33 @@ for line in result:
 
 请参考：[关键信息抽取教程](../kie/README_ch.md)。
 
+<a name="226"></a>
+
+#### 2.2.6 版面恢复
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,save_structure_res
+from paddelocr.ppstructure.recovery.recovery_to_doc import sorted_layout_boxes, convert_info_docx
+
+table_engine = PPStructure(layout=False, show_log=True)
+
+save_folder = './output'
+img_path = 'PaddleOCR/ppstructure/docs/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder, os.path.basename(img_path).split('.')[0])
+
+for line in result:
+    line.pop('img')
+    print(line)
+
+h, w, _ = img.shape
+res = sorted_layout_boxes(res, w)
+convert_info_docx(img, result, save_folder, os.path.basename(img_path).split('.')[0])
+```
+
 <a name="23"></a>
 ### 2.3 返回结果说明
 PP-Structure的返回结果为一个dict组成的list，示例如下
@@ -235,6 +279,7 @@ dict 里各个字段说明如下
 | table  | 前向中是否执行表格识别  | True   |
 | ocr    | 对于版面分析中的非表格区域，是否执行ocr。当layout为False时会被自动设置为False| True |
 | recovery    | 前向中是否执行版面恢复| False |
+| save_pdf | 版面恢复导出docx文件的同时，是否导出pdf文件 | False |
 | structure_version |  模型版本，可选 PP-structure和PP-structurev2  | PP-structure |
 
 大部分参数和PaddleOCR whl包保持一致，见 [whl包文档](../../doc/doc_ch/whl.md)

ppstructure/docs/quickstart_en.md

@@ -8,12 +8,15 @@
     - [2.1.3 layout analysis](#213-layout-analysis)
     - [2.1.4 table recognition](#214-table-recognition)
     - [2.1.5 Key Information Extraction](#215-Key-Information-Extraction)
+    - [2.1.6 layout recovery](#216-layout-recovery)
   - [2.2 Use by code](#22-use-by-code)
     - [2.2.1 image orientation + layout analysis + table recognition](#221-image-orientation--layout-analysis--table-recognition)
     - [2.2.2 layout analysis + table recognition](#222-layout-analysis--table-recognition)
     - [2.2.3 layout analysis](#223-layout-analysis)
     - [2.2.4 table recognition](#224-table-recognition)
+    - [2.2.5 DocVQA](#225-dockie)
     - [2.2.5 Key Information Extraction](#225-Key-Information-Extraction)
+    - [2.2.6 layout recovery](#226-layout-recovery)  
   - [2.3 Result description](#23-result-description)
     - [2.3.1 layout analysis + table recognition](#231-layout-analysis--table-recognition)
     - [2.3.2 Key Information Extraction](#232-Key-Information-Extraction)
@@ -24,14 +27,16 @@
 ## 1. Install package
 
 ```bash
-# Install paddleocr, version 2.5+ is recommended
-pip3 install "paddleocr>=2.5"
+# Install paddleocr, version 2.6 is recommended
+pip3 install "paddleocr>=2.6"
 # Install the KIE dependency packages (if you do not use the KIE, you can skip it)
 pip install -r kie/requirements.txt
-
+# Install the image direction classification dependency package paddleclas (if you do not use the image direction classification, you can skip it)
+pip3 install paddleclas
 ```
 
 <a name="2"></a>
+
 ## 2. Use
 
 <a name="21"></a>
@@ -66,6 +71,12 @@ paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/table.jpg --type=structur
 
 Please refer to: [Key Information Extraction](../kie/README.md) .
 
+<a name="216"></a>
+#### 2.1.6 layout recovery
+```bash
+paddleocr --image_dir=PaddleOCR/ppstructure/docs/table/1.png --type=structure --recovery=true
+```
+
 <a name="22"></a>
 ### 2.2 Use by code
 
@@ -174,6 +185,32 @@ for line in result:
 
 Please refer to: [Key Information Extraction](../kie/README.md) .
 
+<a name="226"></a>
+#### 2.2.6 layout recovery
+
+```python
+import os
+import cv2
+from paddleocr import PPStructure,save_structure_res
+from paddelocr.ppstructure.recovery.recovery_to_doc import sorted_layout_boxes, convert_info_docx
+
+table_engine = PPStructure(layout=False, show_log=True)
+
+save_folder = './output'
+img_path = 'PaddleOCR/ppstructure/docs/table/1.png'
+img = cv2.imread(img_path)
+result = table_engine(img)
+save_structure_res(result, save_folder, os.path.basename(img_path).split('.')[0])
+
+for line in result:
+    line.pop('img')
+    print(line)
+
+h, w, _ = img.shape
+res = sorted_layout_boxes(res, w)
+convert_info_docx(img, result, save_folder, os.path.basename(img_path).split('.')[0])
+```
+
 <a name="23"></a>
 ### 2.3 Result description
 
@@ -235,6 +272,7 @@ Please refer to: [Key Information Extraction](../kie/README.md) .
 | table  | Whether to perform table recognition in forward  | True   |
 | ocr    | Whether to perform ocr for non-table areas in layout analysis. When layout is False, it will be automatically set to False| True |
 | recovery    | Whether to perform layout recovery in forward| False |
+| save_pdf    | Whether to convert docx to pdf when recovery| False |
 | structure_version |  Structure version, optional PP-structure and PP-structurev2  | PP-structure |
 
 Most of the parameters are consistent with the PaddleOCR whl package, see [whl package documentation](../../doc/doc_en/whl.md)

ppstructure/layout/README.md

@@ -63,7 +63,7 @@ python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simp
 git clone https://github.com/PaddlePaddle/PaddleDetection.git
 ```
 
-- **（2）安装其他依赖 **
+- **（2）安装其他依赖**
 
 ```bash
 cd PaddleDetection
@@ -138,7 +138,7 @@ json文件包含所有图像的标注，数据以字典嵌套的方式存放，
 
   ```
   {
-  
+
       'segmentation':             # 物体的分割标注
       'area': 60518.099043117836, # 物体的区域面积
       'iscrowd': 0,               # iscrowd
@@ -166,15 +166,17 @@ json文件包含所有图像的标注，数据以字典嵌套的方式存放，
 
 提供了训练脚本、评估脚本和预测脚本，本节将以PubLayNet预训练模型为例进行讲解。
 
-如果不希望训练，直接体验后面的模型评估、预测、动转静、推理的流程，可以下载提供的预训练模型，并跳过本部分。
+如果不希望训练，直接体验后面的模型评估、预测、动转静、推理的流程，可以下载提供的预训练模型(PubLayNet数据集)，并跳过本部分。
 
 ```
 mkdir pretrained_model
 cd pretrained_model
-# 下载并解压PubLayNet预训练模型
+# 下载PubLayNet预训练模型
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout.pdparams
 ```
 
+下载更多[版面分析模型](../docs/models_list.md)（中文CDLA数据集预训练模型、表格预训练模型）
+
 ### 4.1. 启动训练
 
 开始训练:
@@ -184,7 +186,7 @@ wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_
 如果你希望训练自己的数据集，需要修改配置文件中的数据配置、类别数。
 
 
-以`configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml` 为例，修改的内容如下所示。
+以`configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml` 为例，修改的内容如下所示。
 
 ```yaml
 metric: COCO
@@ -223,16 +225,20 @@ TestDataset:
 # 训练日志会自动保存到 log 目录中
 
 # 单卡训练
+export CUDA_VISIBLE_DEVICES=0
 python3 tools/train.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
 	--eval
 
 # 多卡训练，通过--gpus参数指定卡号
+export CUDA_VISIBLE_DEVICES=0,1,2,3
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
 	--eval
 ```
 
+**注意：**如果训练时显存out memory，将TrainReader中batch_size调小，同时LearningRate中base_lr等比例减小。发布的config均由8卡训练得到，如果改变GPU卡数为1，那么base_lr需要减小8倍。
+
 正常启动训练后，会看到以下log输出：
 
 ```
@@ -254,9 +260,11 @@ PaddleDetection支持了基于FGD([Focal and Global Knowledge Distillation for D
 更换数据集，修改【TODO】配置中的数据配置、类别数，具体可以参考4.1。启动训练：
 
 ```bash
-python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
-	--slim_config configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x2_5_layout.yml \
+# 单卡训练
+export CUDA_VISIBLE_DEVICES=0
+python3 tools/train.py \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
+	--slim_config configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml \
 	--eval
 ```
 
@@ -267,13 +275,13 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py \
 
 ### 5.1. 指标评估
 
-训练中模型参数默认保存在`output/picodet_lcnet_x1_0_layout`目录下。在评估指标时，需要设置`weights`指向保存的参数文件。评估数据集可以通过 `configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml`  修改`EvalDataset`中的 `image_dir`、`anno_path`和`dataset_dir` 设置。
+训练中模型参数默认保存在`output/picodet_lcnet_x1_0_layout`目录下。在评估指标时，需要设置`weights`指向保存的参数文件。评估数据集可以通过 `configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml`  修改`EvalDataset`中的 `image_dir`、`anno_path`和`dataset_dir` 设置。
 
 ```bash
 # GPU 评估， weights 为待测权重
 python3 tools/eval.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
-	-o weigths=./output/picodet_lcnet_x1_0_layout/best_model
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
+	-o weights=./output/picodet_lcnet_x1_0_layout/best_model
 ```
 
 会输出以下信息，打印出mAP、AP0.5等信息。
@@ -299,8 +307,8 @@ python3 tools/eval.py \
 
 ```
 python3 tools/eval.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
-	--slim_config configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x2_5_layout.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
+	--slim_config configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml \
 	-o weights=output/picodet_lcnet_x2_5_layout/best_model
 ```
 
@@ -311,18 +319,17 @@ python3 tools/eval.py \
 ### 5.2. 测试版面分析结果
 
 
-预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml` 完成了模型的训练过程。
-
-使用 PaddleDetection 训练好的模型，您可以使用如下命令进行中文模型预测。
+预测使用的配置文件必须与训练一致，如您通过 `python3 tools/train.py -c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml` 完成了模型的训练过程。
 
+使用 PaddleDetection 训练好的模型，您可以使用如下命令进行模型预测。
 
 ```bash
 python3 tools/infer.py \
-    -c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
+    -c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
     -o weights='output/picodet_lcnet_x1_0_layout/best_model.pdparams' \
     --infer_img='docs/images/layout.jpg' \
     --output_dir=output_dir/ \
-    --draw_threshold=0.4
+    --draw_threshold=0.5
 ```
 
 - `--infer_img`: 推理单张图片，也可以通过`--infer_dir`推理文件中的所有图片。
@@ -335,16 +342,15 @@ python3 tools/infer.py \
 
 ```
 python3 tools/infer.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
-	--slim_config configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x2_5_layout.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
+	--slim_config configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml \
 	-o weights='output/picodet_lcnet_x2_5_layout/best_model.pdparams' \
 	--infer_img='docs/images/layout.jpg' \
 	--output_dir=output_dir/ \
-	--draw_threshold=0.4
+	--draw_threshold=0.5
 ```
 
 
-
 ## 6. 模型导出与预测
 
 
@@ -356,7 +362,7 @@ inference 模型（`paddle.jit.save`保存的模型） 一般是模型训练，
 
 ```bash
 python3 tools/export_model.py \
-	-c configs/picodet/legacy_model/application/layout_detection/picodet_lcnet_x1_0_layout.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
 	-o weights=output/picodet_lcnet_x1_0_layout/best_model \
 	--output_dir=output_inference/
 ```
@@ -377,8 +383,8 @@ FGD蒸馏模型转inference模型步骤如下：
 
 ```bash
 python3 tools/export_model.py \
-	-c configs/picodet/legacy_model/application/publayernet_lcnet_x1_5/picodet_student.yml \
-	--slim_config configs/picodet/legacy_model/application/publayernet_lcnet_x1_5/picodet_teacher.yml \
+	-c configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml \
+	--slim_config configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml \
 	-o weights=./output/picodet_lcnet_x2_5_layout/best_model \
 	--output_dir=output_inference/
 ```
@@ -404,7 +410,7 @@ python3 deploy/python/infer.py \
 ------------------------------------------
 -----------  Model Configuration -----------
 Model Arch: PicoDet
-Transform Order: 
+Transform Order:
 --transform op: Resize
 --transform op: NormalizeImage
 --transform op: Permute
@@ -466,4 +472,3 @@ preprocess_time(ms): 2172.50, inference_time(ms): 11.90, postprocess_time(ms): 1
   year={2022}
 }
 ```
-

ppstructure/layout/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

ppstructure/recovery/README.md

@@ -6,10 +6,12 @@ English | [简体中文](README_ch.md)
     - [2.1 Installation dependencies](#2.1)
     - [2.2 Install PaddleOCR](#2.2)
   - [3. Quick Start](#3)
+    - [3.1 Download models](#3.1)
+    - [3.2 Layout recovery](#3.2)
 
 <a name="1"></a>
 
-## 1.  Introduction
+## 1. Introduction
 
 Layout recovery means that after OCR recognition, the content is still arranged like the original document pictures, and the paragraphs are output to word document in the same order.
 
@@ -17,8 +19,9 @@ Layout recovery combines [layout analysis](../layout/README.md)、[table recogni
 The following figure shows the result：
 
 <div align="center">
-<img src="../docs/table/recovery.jpg"  width = "700" />
+<img src="../docs/recovery/recovery.jpg"  width = "700" />
 </div>
+
 <a name="2"></a>
 
 ## 2. Install
@@ -33,14 +36,14 @@ The following figure shows the result：
 python3 -m pip install --upgrade pip
 
 # GPU installation
-python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle-gpu" -i https://mirror.baidu.com/pypi/simple
 
 # CPU installation
-python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle" -i https://mirror.baidu.com/pypi/simple
 
 ````
 
-For more requirements, please refer to the instructions in [Installation Documentation](https://www.paddlepaddle.org.cn/install/quick).
+For more requirements, please refer to the instructions in [Installation Documentation](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/install/pip/macos-pip_en.html).
 
 <a name="2.2"></a>
 
@@ -67,38 +70,61 @@ python3 -m pip install -r ppstructure/recovery/requirements.txt
 
 ## 3. Quick Start
 
-```python
+<a name="3.1"></a>
+### 3.1 Download models
+
+If input is English document, download English models:
+
+```bash
 cd PaddleOCR/ppstructure
 
 # download model
 mkdir inference && cd inference
 # Download the detection model of the ultra-lightweight English PP-OCRv3 model and unzip it
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar
+https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar && tar xf en_PP-OCRv3_det_infer.tar
 # Download the recognition model of the ultra-lightweight English PP-OCRv3 model and unzip it
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf  ch_PP-OCRv3_rec_infer.tar
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar && tar xf en_PP-OCRv3_rec_infer.tar
 # Download the ultra-lightweight English table inch model and unzip it
-wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf en_ppstructure_mobile_v2.0_SLANet_infer.tar
 # Download the layout model of publaynet dataset and unzip it
-wget 
-https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar picodet_lcnet_x1_0_layout_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar && tar xf picodet_lcnet_x1_0_fgd_layout_infer.tar
 cd ..
-# run
+```
+If input is Chinese document，download Chinese models:
+[Chinese and English ultra-lightweight PP-OCRv3 model](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/README.md#pp-ocr-series-model-listupdate-on-september-8th)、[表格识别模型](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md#22-表格识别模型)、[版面分析模型](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md#1-版面分析模型)
+
+<a name="3.2"></a>
+### 3.2 Layout recovery
+
+
+```bash
 python3 predict_system.py \
     --image_dir=./docs/table/1.png \
     --det_model_dir=inference/en_PP-OCRv3_det_infer \
-    --rec_model_dir=inference/en_PP-OCRv3_rec_infe \
+    --rec_model_dir=inference/en_PP-OCRv3_rec_infer \
     --rec_char_dict_path=../ppocr/utils/en_dict.txt \
-    --output=../output/ \
-    --table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \
+    --table_model_dir=inference/en_ppstructure_mobile_v2.0_SLANet_infer \
     --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
-    --table_max_len=488 \
-    --layout_model_dir=inference/picodet_lcnet_x1_0_layout_infer \
+    --layout_model_dir=inference/picodet_lcnet_x1_0_fgd_layout_infer \
     --layout_dict_path=../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt \
     --vis_font_path=../doc/fonts/simfang.ttf \
     --recovery=True \
-		--save_pdf=False
+    --save_pdf=False \
+    --output=../output/
 ```
 
-After running, the docx  of each picture will be saved in the directory specified by the output field
-
-Recovery table to Word code[table_process.py] reference：https://github.com/pqzx/html2docx.git
\ No newline at end of file
+After running, the docx of each picture will be saved in the directory specified by the output field
+
+Field：
+
+- image_dir：test file测试文件， can be picture, picture directory, pdf file, pdf file directory
+- det_model_dir：OCR detection model path
+- rec_model_dir：OCR recognition model path
+- rec_char_dict_path：OCR recognition dict path. If the Chinese model is used, change to "../ppocr/utils/ppocr_keys_v1.txt". And if you trained the model on your own dataset, change to the trained dictionary
+- table_model_dir：tabel recognition model path
+- table_char_dict_path：tabel recognition dict path. If the Chinese model is used, no need to change
+- layout_model_dir：layout analysis model path
+- layout_dict_path：layout analysis dict path. If the Chinese model is used, change to "../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt"
+- recovery：whether to enable layout of recovery, default False
+- save_pdf：when recovery file, whether to save pdf file, default False
+- output：save the recovery result path

ppstructure/recovery/README_ch.md

@@ -8,19 +8,22 @@
   - [2.2 安装PaddleOCR](#2.2)
 
 - [3. 使用](#3)
+  - [3.1 下载模型](#3.1)
+  - [3.2 版面恢复](#3.2)
 
 
 <a name="1"></a>
 
-## 1.  简介
+## 1. 简介
 
 版面恢复就是在OCR识别后，内容仍然像原文档图片那样排列着，段落不变、顺序不变的输出到word文档中等。
 
-版面恢复结合了[版面分析](../layout/README_ch.md)、[表格识别](../table/README_ch.md)技术，从而更好地恢复图片、表格、标题等内容，下图展示了版面恢复的结果：
+版面恢复结合了[版面分析](../layout/README_ch.md)、[表格识别](../table/README_ch.md)技术，从而更好地恢复图片、表格、标题等内容，支持pdf文档、文档图片格式的输入文件，下图展示了版面恢复的结果：
 
 <div align="center">
-<img src="../docs/table/recovery.jpg"  width = "700" />
+<img src="../docs/recovery/recovery.jpg"  width = "700" />
 </div>
+
 <a name="2"></a>
 
 ## 2. 安装
@@ -35,10 +38,10 @@
 python3 -m pip install --upgrade pip
 
 # GPU安装
-python3 -m pip install "paddlepaddle-gpu>=2.3" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle-gpu" -i https://mirror.baidu.com/pypi/simple
 
 # CPU安装
-python3 -m pip install "paddlepaddle>=2.3" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle" -i https://mirror.baidu.com/pypi/simple
 
 ```
 
@@ -69,40 +72,66 @@ python3 -m pip install -r ppstructure/recovery/requirements.txt
 
 ## 3. 使用
 
-恢复给定文档的版面：
+<a name="3.1"></a>
+
+### 3.1 下载模型
+
+如果输入为英文文档类型，下载英文模型
 
-```python
+```bash
 cd PaddleOCR/ppstructure
 
 # 下载模型
 mkdir inference && cd inference
-# 下载超英文轻量级PP-OCRv3模型的检测模型并解压
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar
-# 下载英文轻量级PP-OCRv3模型的识别模型并解压
-wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf  ch_PP-OCRv3_rec_infer.tar
-# 下载超轻量级英文表格英寸模型并解压
-wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar
+# 下载英文超轻量PP-OCRv3检测模型并解压
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar && tar xf en_PP-OCRv3_det_infer.tar
+# 下载英文超轻量PP-OCRv3识别模型并解压
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar && tar xf en_PP-OCRv3_rec_infer.tar
+# 下载英文表格识别模型并解压
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar && tar xf en_ppstructure_mobile_v2.0_SLANet_infer.tar
 # 下载英文版面分析模型
-wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar && tar picodet_lcnet_x1_0_layout_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar && tar xf picodet_lcnet_x1_0_fgd_layout_infer.tar
 cd ..
+```
+
+如果输入为中文文档类型，在下述链接中下载中文模型即可：
 
-# 执行预测
+[PP-OCRv3中英文超轻量文本检测和识别模型](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/README_ch.md#pp-ocr%E7%B3%BB%E5%88%97%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8%E6%9B%B4%E6%96%B0%E4%B8%AD)、[表格识别模型](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md#22-表格识别模型)、[版面分析模型](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md#1-版面分析模型)
+
+<a name="3.2"></a>
+
+### 3.2 版面恢复
+
+使用下载的模型恢复给定文档的版面，以英文模型为例，执行如下命令：
+
+```bash
 python3 predict_system.py \
     --image_dir=./docs/table/1.png \
     --det_model_dir=inference/en_PP-OCRv3_det_infer \
-    --rec_model_dir=inference/en_PP-OCRv3_rec_infe \
+    --rec_model_dir=inference/en_PP-OCRv3_rec_infer \
     --rec_char_dict_path=../ppocr/utils/en_dict.txt \
-    --output=../output/ \
-    --table_model_dir=inference/ch_ppstructure_mobile_v2.0_SLANet_infer \
+    --table_model_dir=inference/en_ppstructure_mobile_v2.0_SLANet_infer \
     --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
-    --table_max_len=488 \
-    --layout_model_dir=inference/picodet_lcnet_x1_0_layout_infer \
+    --layout_model_dir=inference/picodet_lcnet_x1_0_fgd_layout_infer \
     --layout_dict_path=../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt \
     --vis_font_path=../doc/fonts/simfang.ttf \
     --recovery=True \
-		--save_pdf=False
+    --save_pdf=False \
+    --output=../output/
 ```
 
-运行完成后，每张图片的docx文档会保存到`output`字段指定的目录下
-
-表格恢复到Word代码[table_process.py]来自：https://github.com/pqzx/html2docx.git
+运行完成后，恢复版面的docx文档会保存到`output`字段指定的目录下
+
+字段含义：
+
+- image_dir：测试文件，可以是图片、图片目录、pdf文件、pdf文件目录
+- det_model_dir：OCR检测模型路径
+- rec_model_dir：OCR识别模型路径
+- rec_char_dict_path：OCR识别字典，如果更换为中文模型，需要更改为"../ppocr/utils/ppocr_keys_v1.txt"，如果您在自己的数据集上训练的模型，则更改为训练的字典的文件
+- table_model_dir：表格识别模型路径
+- table_char_dict_path：表格识别字典，如果更换为中文模型，不需要更换字典
+- layout_model_dir：版面分析模型路径
+- layout_dict_path：版面分析字典，如果更换为中文模型，需要更改为"../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt"
+- recovery：是否进行版面恢复，默认False
+- save_pdf：进行版面恢复导出docx文档的同时，是否保存为pdf文件，默认为False
+- output：版面恢复结果保存路径

ppstructure/recovery/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

ppstructure/recovery/recovery_to_doc.py

@@ -24,7 +24,7 @@ from docx.enum.section import WD_SECTION
 from docx.oxml.ns import qn
 from docx.enum.table import WD_TABLE_ALIGNMENT
 
-from table_process import HtmlToDocx
+from ppstructure.recovery.table_process import HtmlToDocx
 
 from ppocr.utils.logging import get_logger
 logger = get_logger()
@@ -69,7 +69,7 @@ def convert_info_docx(img, res, save_folder, img_name, save_pdf):
             new_table = deepcopy(table)
             new_table.alignment = WD_TABLE_ALIGNMENT.CENTER
             paragraph.add_run().element.addnext(new_table._tbl)
-            
+
         else:
             paragraph = doc.add_paragraph()
             paragraph_format = paragraph.paragraph_format
@@ -86,10 +86,10 @@ def convert_info_docx(img, res, save_folder, img_name, save_pdf):
 
     # save to pdf
     if save_pdf:
-        pdf = os.path.join(save_folder, '{}.pdf'.format(img_name))
+        pdf_path = os.path.join(save_folder, '{}.pdf'.format(img_name))
         from docx2pdf import convert
         convert(docx_path, pdf_path)
-        logger.info('pdf save to {}'.format(pdf))
+        logger.info('pdf save to {}'.format(pdf_path))
 
 
 def sorted_layout_boxes(res, w):
@@ -112,7 +112,7 @@ def sorted_layout_boxes(res, w):
     res_left = []
     res_right = []
     i = 0
-    
+
     while True:
         if i >= num_boxes:
             break
@@ -137,7 +137,7 @@ def sorted_layout_boxes(res, w):
             res_left = []
             res_right = []
             break
-        elif _boxes[i]['bbox'][0] < w / 4 and _boxes[i]['bbox'][2] < 3*w / 4:
+        elif _boxes[i]['bbox'][0] < w / 4 and _boxes[i]['bbox'][2] < 3 * w / 4:
             _boxes[i]['layout'] = 'double'
             res_left.append(_boxes[i])
             i += 1
@@ -157,4 +157,4 @@ def sorted_layout_boxes(res, w):
         new_res += res_left
     if res_right:
         new_res += res_right
-    return new_res
\ No newline at end of file
+    return new_res

ppstructure/utility.py

@@ -84,13 +84,18 @@ def init_args():
         type=str2bool,
         default=True,
         help='In the forward, whether the non-table area is recognition by ocr')
+    # param for recovery
     parser.add_argument(
         "--recovery",
-        type=bool,
+        type=str2bool,
         default=False,
         help='Whether to enable layout of recovery')
     parser.add_argument(
-        "--save_pdf", type=bool, default=False, help='Whether to save pdf file')
+        "--save_pdf",
+        type=str2bool,
+        default=False,
+        help='Whether to save pdf file')
+
     return parser