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m986883511 · Apr 25, 2022 · a3d9e72 · a3d9e72
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diff --git a/ppstructure/docs/inference_en.md b/ppstructure/docs/inference_en.md
@@ -1,31 +1,33 @@
-# 基于Python预测引擎推理
+# Python Inference
 
 - [1. Structure](#1)
-  - [1.1 版面分析+表格识别](#1.1)
-  - [1.2 版面分析](#1.2)
-  - [1.3 表格识别](#1.3)
+  - [1.1 layout analysis + table recognition](#1.1)
+  - [1.2 layout analysis](#1.2)
+  - [1.3 table recognition](#1.3)
 - [2. DocVQA](#2)
 
 <a name="1"></a>
 ## 1. Structure
-进入`ppstructure`目录
+Go to the `ppstructure` directory
 
 ```bash
 cd ppstructure
 ````
-下载模型
+
+download model
+
 ```bash
 mkdir inference && cd inference
-# 下载PP-OCRv2文本检测模型并解压
+# Download the PP-OCRv2 text detection model and unzip it
 wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar && tar xf ch_PP-OCRv2_det_slim_quant_infer.tar
-# 下载PP-OCRv2文本识别模型并解压
+# Download the PP-OCRv2 text recognition model and unzip it
 wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar && tar xf ch_PP-OCRv2_rec_slim_quant_infer.tar
-# 下载超轻量级英文表格预测模型并解压
+# Download the ultra-lightweight English table structure 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
 cd ..
 ```
 <a name="1.1"></a>
-### 1.1 版面分析+表格识别
+### 1.1 layout analysis + table recognition
 ```bash
 python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_infer \
                           --rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
@@ -36,17 +38,17 @@ python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_i
                           --output=../output \
                           --vis_font_path=../doc/fonts/simfang.ttf
 ```
-运行完成后，每张图片会在`output`字段指定的目录下的`structure`目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名为表格在图片里的坐标。详细的结果会存储在`res.txt`文件中。
+After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each table in the image will be stored as an excel, and the picture area will be cropped and saved. The filename of excel and picture is their coordinates in the image. Detailed results are stored in the `res.txt` file.
 
 <a name="1.2"></a>
-### 1.2 版面分析
+### 1.2 layout analysis
 ```bash
 python3 predict_system.py --image_dir=./docs/table/1.png --table=false --ocr=false --output=../output/
 ```
-运行完成后，每张图片会在`output`字段指定的目录下的`structure`目录下有一个同名目录，图片区域会被裁剪之后保存下来，图片名为表格在图片里的坐标。版面分析结果会存储在`res.txt`文件中。
+After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each picture in image will be cropped and saved. The filename of picture area is their coordinates in the image. Layout analysis results will be stored in the `res.txt` file
 
 <a name="1.3"></a>
-### 1.3 表格识别
+### 1.3 table recognition
 ```bash
 python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_infer \
                           --rec_model_dir=inference/ch_PP-OCRv2_rec_slim_quant_infer \
@@ -58,17 +60,16 @@ python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv2_det_slim_quant_i
                           --vis_font_path=../doc/fonts/simfang.ttf \
                           --layout=false
 ```
-运行完成后，每张图片会在`output`字段指定的目录下的`structure`目录下有一个同名目录，表格会存储为一个excel，excel文件名为`[0,0,img_h,img_w]`。
+After the operation is completed, each image will have a directory with the same name in the `structure` directory under the directory specified by the `output` field. Each table in the image will be stored as an excel. The filename of excel is their coordinates in the image.
 
 <a name="2"></a>
 ## 2. DocVQA
 
 ```bash
 cd ppstructure
 
-# 下载模型
+# download model
 mkdir inference && cd inference
-# 下载SER xfun 模型并解压
 wget https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar && tar xf PP-Layout_v1.0_ser_pretrained.tar
 cd ..
 
@@ -77,4 +78,4 @@ python3 predict_system.py --model_name_or_path=vqa/PP-Layout_v1.0_ser_pretrained
                           --image_dir=vqa/images/input/zh_val_0.jpg  \
                           --vis_font_path=../doc/fonts/simfang.ttf
 ```
-运行完成后，每张图片会在`output`字段指定的目录下的`vqa`目录下存放可视化之后的图片，图片名和输入图片名一致。
+After the operation is completed, each image will store the visualized image in the `vqa` directory under the directory specified by the `output` field, and the image name is the same as the input image name.
diff --git a/ppstructure/docs/models_list_en.md b/ppstructure/docs/models_list_en.md
@@ -1,56 +1,56 @@
-# PP-Structure 系列模型列表
+# PP-Structure Model list
 
-- [1. 版面分析模型](#1)
-- [2. OCR和表格识别模型](#2)
+- [1. Layout Analysis](#1)
+- [2. OCR and Table Recognition](#2)
     - [2.1 OCR](#21)
-    - [2.2 表格识别模型](#22)
-- [3. VQA模型](#3)
-- [4. KIE模型](#4)
+    - [2.2 Table Recognition](#22)
+- [3. VQA](#3)
+- [4. KIE](#4)
 
 
 <a name="1"></a>
-## 1. 版面分析模型
+## 1. Layout Analysis
 
-|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"}|
+|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"}|
 
 <a name="2"></a>
-## 2. OCR和表格识别模型
+## 2. OCR and Table Recognition
 
 <a name="21"></a>
 ### 2.1 OCR
 
-|模型名称|模型简介|推理模型大小|下载地址|
-| --- | --- | --- | --- |
-|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
-|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
+|model name| description | inference model size |download|
+| --- |---|---| --- |
+|en_ppocr_mobile_v2.0_table_det| Text detection model of English table scenes trained on PubTabNet dataset | 4.7M                |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
+|en_ppocr_mobile_v2.0_table_rec| Text recognition model of English table scenes trained on PubTabNet dataset | 6.9M                |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
 
-如需要使用其他OCR模型，可以在 [PP-OCR model_list](../../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到 `det_model_dir`, `rec_model_dir`两个字段即可。
+If you need to use other OCR models, you can download the model in [PP-OCR model_list](../../doc/doc_ch/models_list.md) or use the model you trained yourself to configure to `det_model_dir`, `rec_model_dir` field.
 
 <a name="22"></a>
-### 2.2 表格识别模型
+### 2.2 Table Recognition
 
-|模型名称|模型简介|推理模型大小|下载地址|
-| --- | --- | --- | --- |
-|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
+|model| description                                                                 |inference model size|download|
+| --- |-----------------------------------------------------------------------------| --- | --- |
+|en_ppocr_mobile_v2.0_table_structure| Table structure model for English table scenes trained on PubTabNet dataset |18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
 
 <a name="3"></a>
-## 3. VQA模型
+## 3. VQA
 
-|模型名称|模型简介|推理模型大小|下载地址|
-| --- | --- | --- | --- |
-|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
-|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
-|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
-|re_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
-|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
+|model| description                                                    |inference model size|download|
+| --- |----------------------------------------------------------------| --- | --- |
+|ser_LayoutXLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLM   |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLM    |1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutXLMv2 |778M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|re_LayoutLMv2_xfun_zh| Re model trained on xfun Chinese dataset based on LayoutXLMv2  |765M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh| SER model trained on xfun Chinese dataset based on LayoutLM    |430M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
 <a name="4"></a>
-## 4. KIE模型
+## 4. KIE
 
-|模型名称|模型简介|模型大小|下载地址|
+|model|description|model size|download|
 | --- | --- | --- | --- |
-|SDMGR|关键信息提取模型|78M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|Key Information Extraction Model|78M|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
diff --git a/ppstructure/docs/quickstart.md b/ppstructure/docs/quickstart.md
@@ -191,7 +191,6 @@ dict 里各个字段说明如下
 | model_name_or_path   | VQA SER模型地址                                                                                                                                        | None                                                    |
 | max_seq_length       | VQA SER模型最大支持token长度                                                                                                                               | 512                                                     |
 | label_map_path       | VQA SER 标签文件地址                                                                                                                                     | ./vqa/labels/labels_ser.txt                             |
-| mode                 | pipeline预测模式，structure: 版面分析+表格识别; VQA: SER文档信息抽取                                                                                                  | structure                                               |
 | layout               | 前向中是否执行版面分析                                                                                                                                        | True                                                    |
 | 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                                                    |