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[doc] update PPOCRLabel doc (#9563)
* add finetune en doc & test=document_fix * fix dead link & test=document_fix * fix dead link & test=document_fix * update check img * fix det res dtype * update args default type & test=document_fix * fix numpy version * support numpy1.24.0 * fix doc & test=document_fix * update doc
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@ -60,7 +60,7 @@ PPOCRLabel can be started in two ways: whl package and Python script. The whl pa
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```bash
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pip install PPOCRLabel # install
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# Select label mode and run
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# Select label mode and run
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PPOCRLabel # [Normal mode] for [detection + recognition] labeling
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PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
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```
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@ -76,7 +76,7 @@ PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extrac
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pip3 install PPOCRLabel
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pip3 install trash-cli
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# Select label mode and run
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# Select label mode and run
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PPOCRLabel # [Normal mode] for [detection + recognition] labeling
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PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
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```
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@ -86,7 +86,7 @@ PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extrac
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pip3 install PPOCRLabel
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pip3 install opencv-contrib-python-headless==4.2.0.32
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# Select label mode and run
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# Select label mode and run
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PPOCRLabel # [Normal mode] for [detection + recognition] labeling
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PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
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```
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@ -97,7 +97,7 @@ If you modify the PPOCRLabel file (for example, specifying a new built-in model)
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```bash
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cd ./PPOCRLabel # Switch to the PPOCRLabel directory
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# Select label mode and run
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# Select label mode and run
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python PPOCRLabel.py # [Normal mode] for [detection + recognition] labeling
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python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
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```
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@ -140,20 +140,20 @@ pip3 install dist/PPOCRLabel-0.0.0-py2.py3-none-any.whl
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10. Labeling result: the user can export the label result manually through the menu "File - Export Label", while the program will also export automatically if "File - Auto export Label Mode" is selected. The manually checked label will be stored in *Label.txt* under the opened picture folder. Click "File"-"Export Recognition Results" in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*<sup>[4]</sup>.
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### 2.2 Table Annotation
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The table annotation is aimed at extracting the structure of the table in a picture and converting it to Excel format,
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The table annotation is aimed at extracting the structure of the table in a picture and converting it to Excel format,
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so the annotation needs to be done simultaneously with external software to edit Excel.
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In PPOCRLabel, complete the text information labeling (text and position), complete the table structure information
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In PPOCRLabel, complete the text information labeling (text and position), complete the table structure information
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labeling in the Excel file, the recommended steps are:
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1. Table annotation: After opening the table picture, click on the `Table Recognition` button in the upper right corner of PPOCRLabel, which will call the table recognition model in PP-Structure to automatically label
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1. Table annotation: After opening the table picture, click on the `Table Recognition` button in the upper right corner of PPOCRLabel, which will call the table recognition model in PP-Structure to automatically label
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the table and pop up Excel at the same time.
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2. Change the recognition result: **label each cell** (i.e. the text in a cell is marked as a box). Right click on the box and click on `Cell Re-recognition`.
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2. Change the recognition result: **label each cell** (i.e. the text in a cell is marked as a box). Right click on the box and click on `Cell Re-recognition`.
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You can use the model to automatically recognise the text within a cell.
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3. Mark the table structure: for each cell contains the text, **mark as any identifier (such as `1`) in Excel**, to ensure that the merged cell structure is same as the original picture.
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> Note: If there are blank cells in the table, you also need to mark them with a bounding box so that the total number of cells is the same as in the image.
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> Note: If there are blank cells in the table, you also need to mark them with a bounding box so that the total number of cells is the same as in the image.
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4. ***Adjust cell order:*** Click on the menu `View` - `Show Box Number` to show the box ordinal numbers, and drag all the results under the 'Recognition Results' column on the right side of the software interface to make the box numbers are arranged from left to right, top to bottom
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@ -209,9 +209,9 @@ labeling in the Excel file, the recommended steps are:
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- Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
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- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude French, German, Korean, and Japanese.
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For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
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For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_en/models_list_en.md)
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- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/PPOCRLabel/PPOCRLabel.py#L86) :
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- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/PPOCRLabel/PPOCRLabel.py#L97) :
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add parameter `det_model_dir` in `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) `
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@ -233,7 +233,7 @@ PPOCRLabel supports three ways to export Label.txt
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```
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cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
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python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
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python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
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```
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Parameter Description:
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@ -255,7 +255,7 @@ PPOCRLabel supports three ways to export Label.txt
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|- word_003.jpg
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| ...
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```
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### 3.5 Error message
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- If paddleocr is installed with whl, it has a higher priority than calling PaddleOCR class with paddleocr.py, which may cause an exception if whl package is not updated.
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@ -105,7 +105,7 @@ python PPOCRLabel.py --lang ch
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```bash
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cd ./PPOCRLabel
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python3 setup.py bdist_wheel
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python3 setup.py bdist_wheel
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pip3 install dist/PPOCRLabel-0.0.0-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
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```
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@ -130,13 +130,13 @@ pip3 install dist/PPOCRLabel-0.0.0-py2.py3-none-any.whl -i https://mirror.baidu.
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表格标注针对表格的结构化提取，将图片中的表格转换为Excel格式，因此标注时需要配合外部软件打开Excel同时完成。在PPOCRLabel软件中完成表格中的文字信息标注（文字与位置）、在Excel文件中完成表格结构信息标注，推荐的步骤为：
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1. 表格识别：打开表格图片后，点击软件右上角 `表格识别` 按钮，软件调用PP-Structure中的表格识别模型，自动为表格打标签，同时弹出Excel
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2. 更改标注结果：**以表格中的单元格为单位增加标注框**（即一个单元格内的文字都标记为一个框）。标注框上鼠标右键后点击 `单元格重识别`
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2. 更改标注结果：**以表格中的单元格为单位增加标注框**（即一个单元格内的文字都标记为一个框）。标注框上鼠标右键后点击 `单元格重识别`
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可利用模型自动识别单元格内的文字。
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> 注意：如果表格中存在空白单元格，同样需要使用一个标注框将其标出，使得单元格总数与图像中保持一致。
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3. **调整单元格顺序**：点击软件`视图-显示框编号` 打开标注框序号，在软件界面右侧拖动 `识别结果` 一栏下的所有结果，使得标注框编号按照从左到右，从上到下的顺序排列，按行依次标注。
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4. 标注表格结构：**在外部Excel软件中，将存在文字的单元格标记为任意标识符（如 `1` ）**，保证Excel中的单元格合并情况与原图相同即可（即不需要Excel中的单元格文字与图片中的文字完全相同）
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5. 导出JSON格式：关闭所有表格图像对应的Excel，点击 `文件`-`导出表格标注`，生成gt.txt标注文件。
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@ -191,9 +191,9 @@ pip3 install dist/PPOCRLabel-0.0.0-py2.py3-none-any.whl -i https://mirror.baidu.
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- 默认模型：PPOCRLabel默认使用PaddleOCR中的中英文超轻量OCR模型，支持中英文与数字识别，多种语言检测。
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- 模型语言切换：用户可通过菜单栏中 "PaddleOCR" - "选择模型" 切换内置模型语言，目前支持的语言包括法文、德文、韩文、日文。具体模型下载链接可参考[PaddleOCR模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md).
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- 模型语言切换：用户可通过菜单栏中 "PaddleOCR" - "选择模型" 切换内置模型语言，目前支持的语言包括法文、德文、韩文、日文。具体模型下载链接可参考[PaddleOCR模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/models_list.md).
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- **自定义模型**：如果用户想将内置模型更换为自己的推理模型，可根据[自定义模型代码使用](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md#%E8%87%AA%E5%AE%9A%E4%B9%89%E6%A8%A1%E5%9E%8B)，通过修改PPOCRLabel.py中针对[PaddleOCR类的实例化](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/PPOCRLabel.py#L116) 实现，例如指定检测模型：`self.ocr = PaddleOCR(det=True, cls=True, use_gpu=gpu, lang=lang) `，在 `det_model_dir` 中传入 自己的模型即可。
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- **自定义模型**：如果用户想将内置模型更换为自己的推理模型，可根据[自定义模型代码使用](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/whl.md#3-%E8%87%AA%E5%AE%9A%E4%B9%89%E6%A8%A1%E5%9E%8B)，通过修改PPOCRLabel.py中针对[PaddleOCR类的实例化](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/PPOCRLabel/PPOCRLabel.py#L97) 或者[PPStructure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/PPOCRLabel/PPOCRLabel.py#L104)实现，例如指定检测模型：`self.ocr = PaddleOCR(det=True, cls=True, use_gpu=gpu, lang=lang) `，在 `det_model_dir` 中传入自己的模型即可。
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### 3.3 导出标记结果
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@ -213,7 +213,7 @@ PPOCRLabel支持三种导出方式：
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```
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cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
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python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
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python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../train_data
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```
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参数说明：
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@ -235,7 +235,7 @@ python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --datasetRootPath ../
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|- word_003.jpg
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| ...
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```
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### 3.5 错误提示
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- 如果同时使用whl包安装了paddleocr，其优先级大于通过paddleocr.py调用PaddleOCR类，whl包未更新时会导致程序异常。
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