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@@ -18,6 +18,11 @@ PaddleX offers two ways to experience the pipeline: one is through the PaddleX w
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```bash
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paddlex --pipeline OCR \
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--input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/practical_tutorial/OCR_rec/case.png
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+ --use_doc_orientation_classify False \
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+ --use_doc_unwarping False \
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+ --use_textline_orientation False \
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+ --device gpu:0 \
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+ --save_path ./output/
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```
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- AIStudio Community Experience: Go to [Baidu AIStudio Community](https://aistudio.baidu.com/pipeline/mine), click "Create Pipeline", and create a <b>Universal OCR</b> pipeline for a quick trial.
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@@ -87,7 +92,7 @@ tar -xf ./dataset/handwrite_chinese_text_rec.tar -C ./dataset/
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To verify the dataset, simply use the following command:
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```bash
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-python main.py -c paddlex/configs/text_recognition/PP-OCRv4_server_rec.yaml \
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+python main.py -c paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec.yaml \
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-o Global.mode=check_dataset \
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-o Global.dataset_dir=./dataset/handwrite_chinese_text_rec
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```
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@@ -128,7 +133,7 @@ After executing the above command, PaddleX will verify the dataset and collect b
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"analysis": {
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"histogram": "check_dataset\/histogram.png"
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},
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- "dataset_path": "\/mnt\/liujiaxuan01\/new\/new2\/handwrite_chinese_text_rec",
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+ "dataset_path": "handwrite_chinese_text_rec",
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"show_type": "image",
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"dataset_type": "MSTextRecDataset"
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}
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@@ -172,7 +177,7 @@ During data splitting, the original annotation files will be renamed to `xxx.bak
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Before training, ensure that you have validated your dataset. To complete PaddleX model training, simply use the following command:
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```bash
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-python main.py -c paddlex/configs/text_recognition/PP-OCRv4_server_rec.yaml \
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+python main.py -c paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec.yaml \
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-o Global.mode=train \
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-o Global.dataset_dir=./dataset/handwrite_chinese_text_rec
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```
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@@ -209,7 +214,7 @@ After completing model training, all outputs are saved in the specified output d
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After completing model training, you can evaluate the specified model weight file on the validation set to verify the model accuracy. To evaluate a model using PaddleX, simply use the following command:
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```bash
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-python main.py -c paddlex/configs/text_recognition/PP-OCRv4_server_rec.yaml \
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+python main.py -c paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec.yaml \
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-o Global.mode=evaluate \
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-o Global.dataset_dir=./dataset/handwrite_chinese_text_rec
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```
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@@ -307,10 +312,10 @@ Next, based on a learning rate of 0.0002, we can increase the number of training
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Replace the model in the production line with the fine-tuned model for testing, for example:
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```bash
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-paddlex --pipeline OCR \
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- --model PP-OCRv4_server_det PP-OCRv4_server_rec \
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- --model_dir None output/best_accuracy/inference \
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- --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/practical_tutorial/OCR_rec/case.png
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+python main.py -c paddlex/configs/modules/text_recognition/PP-OCRv4_server_rec.yaml \
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+ -o Global.mode=predict \
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+ -o Predict.model_dir="output/best_model/inference" \
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+ -o Predict.input=https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/practical_tutorial/OCR_rec/case.png
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```
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The prediction results will be generated under `./output`, and the prediction result for `case.jpg` is shown below:
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@@ -322,16 +327,52 @@ The prediction results will be generated under `./output`, and the prediction re
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## 7. Development Integration/Deployment
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If the general OCR pipeline meets your requirements for inference speed and accuracy in the production line, you can proceed directly with development integration/deployment.
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-1. Directly apply the trained model in your Python project by referring to the following sample code, and modify the `Pipeline.model` in the `paddlex/pipelines/OCR.yaml` configuration file to your own model path:
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+
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+1. If you need to use the fine-tuned model weights, you can obtain the configuration file for the OCR pipeline and load it for prediction. You can execute the following command to save the results in my_path:
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+
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+```
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+paddlex --get_pipeline_config OCR --save_path ./my_path
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+```
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+
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+Fill in the local path of the fine-tuned model weights into the `model_dir` field in the configuration file. If you need to directly apply the OCR pipeline to your Python project, you can refer to the following example:
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+
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+```yaml
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+pipeline_name: OCR
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+
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+text_type: general
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+
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+use_doc_preprocessor: True
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+use_textline_orientation: True
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+
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+SubPipelines:
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+ DocPreprocessor:
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+ ...
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+
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+SubModules:
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+ TextDetection:
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+ ...
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+ TextLineOrientation:
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+ ...
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+ TextRecognition:
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+ module_name: text_recognition
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+ model_name: PP-OCRv4_mobile_rec
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+ model_dir: null # 此处替换为您训练后得到的模型权重本地路径
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+ batch_size: 1
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+ score_thresh: 0.0
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+```
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+
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+Subsequently, in your Python code, you can utilize the pipeline as follows:
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+
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```python
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from paddlex import create_pipeline
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-pipeline = create_pipeline(pipeline="paddlex/pipelines/OCR.yaml")
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+pipeline = create_pipeline(pipeline="my_path/OCR.yaml")
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output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/doc_images/practical_tutorial/OCR_rec/case.png")
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for res in output:
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res.print() # Print the structured output of the prediction
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res.save_to_img("./output/") # Save the visualized image of the result
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res.save_to_json("./output/") # Save the structured output of the prediction
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```
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+
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For more parameters, please refer to the [General OCR Pipeline Usage Tutorial](../pipeline_usage/tutorials/ocr_pipelines/OCR.en.md).
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2. Additionally, PaddleX offers three other deployment methods, detailed as follows:
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