add PP-StructureV2 (#5558)

* add PP-OCRv2 * add PP-OCRv2 benckmark * update app * update introduction * update PP-OCRv2 introduction * add pipeline of PP-OCRv2 * add PP-OCRv3 * update benckmark * add paddleocr to requirements * update * support paddleocr 2.6.0.1 * rm print code * add image download * add model train * update PP-OCRv3 ref * update doc * update doc * update PP-OCR info * add PP-StructureV2 * update doc * merge upstream * update info.yaml

add PP-StructureV2 (#5558)
* add PP-OCRv2 * add PP-OCRv2 benckmark * update app * update introduction * update PP-OCRv2 introduction * add pipeline of PP-OCRv2 * add PP-OCRv3 * update benckmark * add paddleocr to requirements * update * support paddleocr 2.6.0.1 * rm print code * add image download * add model train * update PP-OCRv3 ref * update doc * update doc * update PP-OCR info * add PP-StructureV2 * update doc * merge upstream * update info.yaml
52970f04 · 文幕地方 · GitHub · 499de374 · 52970f04 · 52970f04
16 changed file
--- a/modelcenter/PP-OCRv2/info.yaml
+++ b/modelcenter/PP-OCRv2/info.yaml
 ---
 Model_Info:
   name: "PP-OCRv2"
-   description: ""
+   description: "PP-OCRv2文字检测识别系统"
-   description_en: ""
+   description_en: "PP-OCRv2 text detection and recognition system"
   icon: "@后续UE统一设计之后，会存到bos上某个位置"
   from_repo: "PaddleOCR"
 Task:
   - tag_en: "CV"
     tag: "计算机视觉"
-     sub_tag_en: "Character Recognition"
+     sub_tag_en: "Text Detection, Character Recognition, Optical Character Recognition"
-     sub_tag: "文字识别"
+     sub_tag: "文字检测，文字识别，OCR"
 Example:
   - title: "《动手学OCR》系列课程之：PP-OCRv2预测部署实战"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/3552922?channelType=0&channel=0"
+     title_en: "Dive into OCR series of courses: PP-OCRv2 prediction and deployment"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/3552922?channelType=0&channel=0"
   - title: "《动手学OCR》系列课程之：OCR文本识别实战"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/3552051?channelType=0&channel=0"
+     title_en: "Dive into OCR series of courses: text recognition in practice"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/3552051?channelType=0&channel=0"
   - title: "《动手学OCR》系列课程之：OCR文本检测实践"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/3551779?channelType=0&channel=0"
+     title_en: "Dive into OCR series of courses: text detection in practice"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/3551779?channelType=0&channel=0"
 Datasets: "ICDAR 2015, ICDAR2019-LSVT,ICDAR2017-RCTW-17,Total-Text,ICDAR2019-ArT"
 Pulisher: "Baidu"
 License: "apache.2.0"
 Paper:
   - title: "PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System"
-     url: "https://arxiv.org/pdf/2109.03144v2.pdf"
+     url: "https://arxiv.org/abs/2109.03144"
 IfTraining: 0
 IfOnlineDemo: 1
--- a/modelcenter/PP-OCRv2/introduction_cn.ipynb
+++ b/modelcenter/PP-OCRv2/introduction_cn.ipynb
@@ -196,7 +196,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.13 ('py38')",
   "language": "python",
   "name": "python3"
  },
@@ -210,7 +210,12 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "58fd1890da6594cebec461cf98c6cb9764024814357f166387d10d267624ecd6"
+   }
  }
 },
 "nbformat": 4,

--- a/modelcenter/PP-OCRv2/introduction_en.ipynb
+++ b/modelcenter/PP-OCRv2/introduction_en.ipynb
@@ -112,7 +112,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### 3.2 Train the model.\n",
+    "### 3.2 Train the model\n",
    "The PP-OCR system consists of a text detection model, an angle classifier and a text recognition model. For the three model training tutorials, please refer to the following documents:\n",
    "1. text detection model: [text detection training tutorial](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/detection.md)\n",
    "1. angle classifier: [angle classifier training tutorial](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/angle_class.md)\n",
@@ -130,6 +130,7 @@
   "source": [
    "## 4. Model Principles\n",
    "\n",
+    "The enhancement strategies are as follows\n",
    "\n",
    "1. Text detection enhancement strategies\n",
    "- Adopt CML (Collaborative Mutual Learning) collaborative mutual learning knowledge distillation strategy.\n",
@@ -193,7 +194,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.13 ('py38')",
   "language": "python",
   "name": "python3"
  },
@@ -207,7 +208,12 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "58fd1890da6594cebec461cf98c6cb9764024814357f166387d10d267624ecd6"
+   }
  }
 },
 "nbformat": 4,

--- a/modelcenter/PP-OCRv3/info.yaml
+++ b/modelcenter/PP-OCRv3/info.yaml
 ---
 Model_Info:
   name: "PP-OCRv3"
-   description: ""
+   description: "PP-OCRv3文字检测识别系统"
-   description_en: ""
+   description_en: "PP-OCRv3 text detection and recognition system"
   icon: "@后续UE统一设计之后，会存到bos上某个位置"
   from_repo: "PaddleOCR"
 Task:
   - tag_en: "CV"
     tag: "计算机视觉"
-     sub_tag_en: "Character Recognition"
+     sub_tag_en: "Text Detection, Character Recognition, Optical Character Recognition"
-     sub_tag: "文字识别"
+     sub_tag: "文字检测，文字识别，OCR"
 Example:
-   - title: "《【官方】十分钟完成 PP-OCRv3 识别全流程实战"
+   - title: "【官方】十分钟完成 PP-OCRv3 识别全流程实战"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/3916206?channelType=0&channel=0"
+     title_en: "[Official] Complete the whole process of PP-OCRv3 identification in ten minutes"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/3916206?channelType=0&channel=0"
   - title: "鸟枪换炮！基于PP-OCRv3的电表检测识别"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/511591?channelType=0&channel=0"
+     title_en: "Swap the shotgun! Detection and recognition electricity meters based on PP-OCRv3"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/511591?channelType=0&channel=0"
   - title: "基于PP-OCRv3实现PCB字符识别"
     url: "https://aistudio.baidu.com/aistudio/projectdetail/4008973?channelType=0&channel=0"
+     title_en: "PCB character recognition based on PP-OCRv3"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/4008973?channelType=0&channel=0"
 Datasets: "ICDAR 2015, ICDAR2019-LSVT,ICDAR2017-RCTW-17,Total-Text,ICDAR2019-ArT"
 Pulisher: "Baidu"
 License: "apache.2.0"

--- a/modelcenter/PP-OCRv3/introduction_cn.ipynb
+++ b/modelcenter/PP-OCRv3/introduction_cn.ipynb
@@ -67,7 +67,7 @@
   "source": [
    "## 3. 模型如何使用\n",
    "\n",
-    "### 3.1 模型推理：\n",
+    "### 3.1 模型推理\n",
    "* 安装PaddleOCR whl包"
   ]
  },
@@ -96,7 +96,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "tags": []
@@ -136,7 +136,7 @@
    "模型训练完成后，可以通过指定模型路径的方式串联使用\n",
    "命令参考如下：\n",
    "```python\n",
-    "paddleocr --image_dir 11.jpg --use_angle_cls true --ocr_version PP-OCRv2 --det_model_dir=/path/to/det_inference_model --cls_model_dir=/path/to/cls_inference_model --rec_model_dir=/path/to/rec_inference_model\n",
+    "paddleocr --image_dir 11.jpg --use_angle_cls true --det_model_dir=/path/to/det_inference_model --cls_model_dir=/path/to/cls_inference_model --rec_model_dir=/path/to/rec_inference_model\n",
    "```"
   ]
  },
@@ -228,36 +228,11 @@
   "source": [
    "## 6. 相关论文以及引用信息\n",
    "```\n",
-    "@article{du2021pp,\n",
+    "@article{li2022pp,\n",
-    "  title={PP-OCRv2: bag of tricks for ultra lightweight OCR system},\n",
+    "  title={PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System},\n",
-    "  author={Du, Yuning and Li, Chenxia and Guo, Ruoyu and Cui, Cheng and Liu, Weiwei and Zhou, Jun and Lu, Bin and Yang, Yehua and Liu, Qiwen and Hu, Xiaoguang and others},\n",
+    "  author={Li, Chenxia and Liu, Weiwei and Guo, Ruoyu and Yin, Xiaoting and Jiang, Kaitao and Du, Yongkun and Du, Yuning and Zhu, Lingfeng and Lai, Baohua and Hu, Xiaoguang and others},\n",
-    "  journal={arXiv preprint arXiv:2109.03144},\n",
+    "  journal={arXiv preprint arXiv:2206.03001},\n",
-    "  year={2021}\n",
+    "  year={2022}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{zhang2018deep,\n",
-    "  title={Deep mutual learning},\n",
-    "  author={Zhang, Ying and Xiang, Tao and Hospedales, Timothy M and Lu, Huchuan},\n",
-    "  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n",
-    "  pages={4320--4328},\n",
-    "  year={2018}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{hu2020gtc,\n",
-    "  title={Gtc: Guided training of ctc towards efficient and accurate scene text recognition},\n",
-    "  author={Hu, Wenyang and Cai, Xiaocong and Hou, Jun and Yi, Shuai and Lin, Zhiping},\n",
-    "  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n",
-    "  volume={34},\n",
-    "  number={07},\n",
-    "  pages={11005--11012},\n",
-    "  year={2020}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{zhang2022context,\n",
-    "  title={Context-based Contrastive Learning for Scene Text Recognition},\n",
-    "  author={Zhang, Xinyun and Zhu, Binwu and Yao, Xufeng and Sun, Qi and Li, Ruiyu and Yu, Bei},\n",
-    "  year={2022},\n",
-    "  organization={AAAI}\n",
    "}\n",
    "```\n"
   ]
@@ -265,7 +240,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.13 ('py38')",
   "language": "python",
   "name": "python3"
  },
@@ -279,7 +254,12 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "58fd1890da6594cebec461cf98c6cb9764024814357f166387d10d267624ecd6"
+   }
  }
 },
 "nbformat": 4,

--- a/modelcenter/PP-OCRv3/introduction_en.ipynb
+++ b/modelcenter/PP-OCRv3/introduction_en.ipynb
@@ -129,7 +129,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### 3.2 Train the model.\n",
+    "### 3.2 Train the model\n",
    "The PP-OCR system consists of a text detection model, an angle classifier and a text recognition model. For the three model training tutorials, please refer to the following documents:\n",
    "1. text detection model: [text detection training tutorial](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/detection.md)\n",
    "1. angle classifier: [angle classifier training tutorial](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.6/doc/doc_ch/angle_class.md)\n",
@@ -137,7 +137,7 @@
    "\n",
    "After the model training is completed, it can be used in series by specifying the model path. The command reference is as follows:\n",
    "```python\n",
-    "paddleocr --image_dir 11.jpg --use_angle_cls true --ocr_version PP-OCRv2 --det_model_dir=/path/to/det_inference_model --cls_model_dir=/path/to/cls_inference_model --rec_model_dir=/path/to/rec_inference_model\n",
+    "paddleocr --image_dir 11.jpg --use_angle_cls true --det_model_dir=/path/to/det_inference_model --cls_model_dir=/path/to/cls_inference_model --rec_model_dir=/path/to/rec_inference_model\n",
    "```"
   ]
  },
@@ -147,7 +147,7 @@
   "source": [
    "## 4. Model Principles\n",
    "\n",
-    "The optimization ideas are as follows\n",
+    "The enhancement strategies are as follows\n",
    "\n",
    "1. Text detection enhancement strategies\n",
    "- LK-PAN: a PAN module with large receptive field\n",
@@ -231,36 +231,11 @@
   "source": [
    "## 6. Related papers and citations\n",
    "```\n",
-    "@article{du2021pp,\n",
+    "@article{li2022pp,\n",
-    "  title={PP-OCRv2: bag of tricks for ultra lightweight OCR system},\n",
+    "  title={PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System},\n",
-    "  author={Du, Yuning and Li, Chenxia and Guo, Ruoyu and Cui, Cheng and Liu, Weiwei and Zhou, Jun and Lu, Bin and Yang, Yehua and Liu, Qiwen and Hu, Xiaoguang and others},\n",
+    "  author={Li, Chenxia and Liu, Weiwei and Guo, Ruoyu and Yin, Xiaoting and Jiang, Kaitao and Du, Yongkun and Du, Yuning and Zhu, Lingfeng and Lai, Baohua and Hu, Xiaoguang and others},\n",
-    "  journal={arXiv preprint arXiv:2109.03144},\n",
+    "  journal={arXiv preprint arXiv:2206.03001},\n",
-    "  year={2021}\n",
+    "  year={2022}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{zhang2018deep,\n",
-    "  title={Deep mutual learning},\n",
-    "  author={Zhang, Ying and Xiang, Tao and Hospedales, Timothy M and Lu, Huchuan},\n",
-    "  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n",
-    "  pages={4320--4328},\n",
-    "  year={2018}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{hu2020gtc,\n",
-    "  title={Gtc: Guided training of ctc towards efficient and accurate scene text recognition},\n",
-    "  author={Hu, Wenyang and Cai, Xiaocong and Hou, Jun and Yi, Shuai and Lin, Zhiping},\n",
-    "  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n",
-    "  volume={34},\n",
-    "  number={07},\n",
-    "  pages={11005--11012},\n",
-    "  year={2020}\n",
-    "}\n",
-    "\n",
-    "@inproceedings{zhang2022context,\n",
-    "  title={Context-based Contrastive Learning for Scene Text Recognition},\n",
-    "  author={Zhang, Xinyun and Zhu, Binwu and Yao, Xufeng and Sun, Qi and Li, Ruiyu and Yu, Bei},\n",
-    "  year={2022},\n",
-    "  organization={AAAI}\n",
    "}\n",
    "```\n"
   ]
@@ -268,7 +243,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.8.13 ('py38')",
   "language": "python",
   "name": "python3"
  },
@@ -282,7 +257,12 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "58fd1890da6594cebec461cf98c6cb9764024814357f166387d10d267624ecd6"
+   }
  }
 },
 "nbformat": 4,

--- a/modelcenter/PP-StructureV2/APP/app.py
+++ b/modelcenter/PP-StructureV2/APP/app.py
+import gradio as gr
+import base64
+from io import BytesIO
+from PIL import Image
+from paddleocr import PPStructure
+table_engine = PPStructure(layout=False, show_log=True)
+def image_to_base64(image):
+    # 输入为PIL读取的图片，输出为base64格式
+    byte_data = BytesIO()  # 创建一个字节流管道
+    image.save(byte_data, format="JPEG")  # 将图片数据存入字节流管道
+    byte_data = byte_data.getvalue()  # 从字节流管道中获取二进制
+    base64_str = base64.b64encode(byte_data).decode("ascii")  # 二进制转base64
+    return base64_str
+# UGC: Define the inference fn() for your models
+def model_inference(image):
+    result = table_engine(image)
+    res = result[0]['res']['html']
+    json_out = {"result": res}
+    return res, json_out
+def clear_all():
+    return None, None, None
+with gr.Blocks() as demo:
+    gr.Markdown("PP-StructureV2")
+    with gr.Column(scale=1, min_width=100):
+        img_in = gr.Image(
+            value="https://user-images.githubusercontent.com/12406017/200574299-32537341-c329-42a5-ae41-35ee4bd43f2f.png",
+            label="Input")
+        with gr.Row():
+            btn1 = gr.Button("Clear")
+            btn2 = gr.Button("Submit")
+        html_out = gr.HTML(label="Output")
+        json_out = gr.JSON(label="jsonOutput")
+    btn2.click(fn=model_inference, inputs=img_in, outputs=[html_out, json_out])
+    btn1.click(fn=clear_all, inputs=None, outputs=[img_in, html_out, json_out])
+    gr.Button.style(1)
+demo.launch()
--- a/modelcenter/PP-StructureV2/APP/app.yml
+++ b/modelcenter/PP-StructureV2/APP/app.yml
+【PP-StructureV2-App-YAML】
+APP_Info:
+    title: PP-StructureV2-App
+    colorFrom: blue
+    colorTo: yellow
+    sdk: gradio
+    sdk_version: 3.4.1
+    app_file: app.py
+    license: apache-2.0
+    device: cpu
\ No newline at end of file
--- a/modelcenter/PP-StructureV2/APP/requirements.txt
+++ b/modelcenter/PP-StructureV2/APP/requirements.txt
+gradio
+paddlepaddle
+paddleocr>=2.6.1.0
--- a/modelcenter/PP-StructureV2/.gitkeep
+++ b/modelcenter/PP-StructureV2/.gitkeep
--- a/modelcenter/PP-StructureV2/benchmark_en.md
+++ b/modelcenter/PP-StructureV2/benchmark_en.md
--- a/modelcenter/PP-StructureV2/download_cn.md
+++ b/modelcenter/PP-StructureV2/download_cn.md
+# 模型列表
+## 1. 版面分析模型
+|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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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 | 221.0M | [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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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 | 221.0M | [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 | 221.0M | [推理模型](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | 同上 |
+## 2. OCR和表格识别模型
+### 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|PubTabNet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|PubTabNet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) |
+如需要使用其他OCR模型，可以在 [PP-OCR model_list](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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。
+### 2.2 表格识别模型
+|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|基于TableRec-RARE在PubTabNet数据集上训练的英文表格识别模型|6.8M|[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) |
+|en_ppstructure_mobile_v2.0_SLANet|基于SLANet在PubTabNet数据集上训练的英文表格识别模型|9.2M|[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_train.tar) |
+|ch_ppstructure_mobile_v2.0_SLANet|基于SLANet的中文表格识别模型|9.3M|[推理模型](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar) |
+<a name="3"></a>
--- a/modelcenter/PP-StructureV2/download_en.md
+++ b/modelcenter/PP-StructureV2/download_en.md
+# Model list
+## 1. Layout Analysis
+|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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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 | 221.0M | [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) | same 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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/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 | 221.0M | [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 | 221.0M                 | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) | same as above |
+## 2. OCR and Table Recognition
+### 2.1 OCR
+|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) |
+If you need to use other OCR models, you can download the model in [PP-OCR model_list](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_en/models_list_en.md) or use the model you trained yourself to configure to `det_model_dir`, `rec_model_dir` field.
+<a name="22"></a>
+### 2.2 Table Recognition
+|model| description                                                                 |inference model size|download|
+| --- |-----------------------------------------------------------------------------| --- | --- |
+|en_ppocr_mobile_v2.0_table_structure| English table recognition model trained on PubTabNet dataset based on TableRec-RARE |6.8M|[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) |
+|en_ppstructure_mobile_v2.0_SLANet|English table recognition model trained on PubTabNet dataset based on SLANet|9.2M|[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_train.tar) |
+|ch_ppstructure_mobile_v2.0_SLANet|Chinese table recognition model based on SLANet|9.3M|[inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_train.tar) |
--- a/modelcenter/PP-StructureV2/info.yaml
+++ b/modelcenter/PP-StructureV2/info.yaml
+---
+Model_Info:
+   name: "PP-StructureV2"
+   description: "PP-StructureV2文档分析系统，包含版面分析，表格识别，版面恢复和关键信息抽取"
+   description_en: "PP-StructureV2 document analysis system, including layout analysis, table recognition, layout recovery and key information extraction"
+   icon: "@后续UE统一设计之后，会存到bos上某个位置"
+   from_repo: "PaddleOCR"
+Task:
+   - tag_en: "CV"
+     tag: "计算机视觉"
+     sub_tag_en: "Layout Analysis, Table Recognition, Layout Recovery, Key Information Extraction"
+     sub_tag: "版面分析，表格识别，版面恢复，关键信息提取"
+Example:
+   - title: "表格识别实战"
+     url: "https://aistudio.baidu.com/aistudio/projectdetail/4770296?channelType=0&channel=0"
+     title_en: "table recognition"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/4770296?channelType=0&channel=0"
+   - title: "OCR发票关键信息抽取"
+     url: "https://aistudio.baidu.com/aistudio/projectdetail/4823162?channelType=0&channel=0"
+     title_en: "Invoice key information extraction"
+     url_en: "https://aistudio.baidu.com/aistudio/projectdetail/4823162?channelType=0&channel=0"
+Datasets: "ICDAR 2015, ICDAR2019-LSVT,ICDAR2017-RCTW-17,Total-Text,ICDAR2019-ArT"
+Pulisher: "Baidu"
+License: "apache.2.0"
+Paper:
+   - title: "PP-StructureV2: A Stronger Document Analysis System"
+     url: "https://arxiv.org/abs/2210.05391v2"
+IfTraining: 0
+IfOnlineDemo: 1
--- a/modelcenter/PP-StructureV2/introduction_cn.ipynb
+++ b/modelcenter/PP-StructureV2/introduction_cn.ipynb
--- a/modelcenter/PP-StructureV2/introduction_en.ipynb
+++ b/modelcenter/PP-StructureV2/introduction_en.ipynb