blazordevlab/paddleocr-backend repository overview

⁠Interactive BBOX OCR using PaddleOCR

Using an OCR engine for Interactive ML-Assisted Labelling, this functionality can speed up annotation for layout detection, classification and recognition models.

PaddleOCR is used for OCR but minimal adaptation is needed to connect other OCR engines or models.

Tested againt Label Studio 1.10.1, with basic support for both Label Studio Local File Storage and S3-compatible storage, with a example data storage with Minio.

⁠Setup process

1. Download and install Docker with Docker Compose. For MacOS and Windows users, we suggest using Docker Desktop. You will also need to have git installed.

2. Launch LabelStudio.

   docker run -it \
      -p 8080:8080 \
      -v `pwd`/mydata:/label-studio/data \
      heartexlabs/label-studio:latest
   

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   Optionally, you may enable local file serving in Label Studio

   docker run -it \
      -p 8080:8080 \
      -v `pwd`/mydata:/label-studio/data \
      --env LABEL_STUDIO_LOCAL_FILES_SERVING_ENABLED=true \
      --env LABEL_STUDIO_LOCAL_FILES_DOCUMENT_ROOT=/label-studio/data/images \
      heartexlabs/label-studio:latest
   

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   If you're using local file serving, be sure to get a copy of the API token from Label Studio to connect the model.

3. Create a new project for PaddleOCR. In the project Settings set up the Labeling Interface.

   Fill in the following template code. It's important to specify smart="true" in RectangleLabels.

   <View>    
      <Image name="image" value="$ocr" zoom="true" zoomControl="false"
            rotateControl="true" width="100%" height="100%"
            maxHeight="auto" maxWidth="auto"/>
      
      <RectangleLabels name="bbox" toName="image" strokeWidth="1" smart="true">
         <Label value="Label1" background="green"/>
         <Label value="Label2" background="blue"/>
         <Label value="Label3" background="red"/>
      </RectangleLabels>
   
      <TextArea name="transcription" toName="image" 
      editable="true" perRegion="true" required="false" 
      maxSubmissions="1" rows="5" placeholder="Recognized Text" 
      displayMode="region-list"/>
   </View>
   

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4. Download the Label Studio Machine Learning backend backend repository.

   git clone https://github.com/humansignal/label-studio-ml-backend
   cd label-studio-ml-backend/label_studio_ml/examples/paddleocr
   

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5. Configure the backend and the Minio server by editing the example.env file. If you opted to use Label Studio Local File Storage, be sure to set the LABEL_STUDIO_HOST and LABEL_STUDIO_ACCESS_TOKEN variables. If you're using the Minio storage example, set the MINIO_ROOT_USER AND MINIO_ROOT_PASSWORD variables, and make the AWS_ACCESS_KEY_ID AND AWS_SECRET_ACCESS_KEY variables equal to those values. You may optionally connect to your own AWS cloud storage by setting those variables. Note that you may need to make additional software changes to the paddleocr_ch.py file to match your particular infrastructure configuration.

   LABEL_STUDIO_HOST=http://host.docker.internal:8080
   LABEL_STUDIO_ACCESS_TOKEN=<optional token for local file access>
   
   AWS_ACCESS_KEY_ID=<set to MINIO_ROOT_USER for minio example>
   AWS_SECRET_ACCESS_KEY=<set to MINIO_ROOT_PASSWORD for minio example>
   AWS_ENDPOINT=http://host.docker.internal:9000
   
   MINIO_ROOT_USER=<username>
   MINIO_ROOT_PASSWORD=<password>
   MINIO_API_CORS_ALLOW_ORIGIN=*
   
   OCR_LANGUAGE=<Language Abbreviation ch,en,fr,japan>
   

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6. Start the Label-Stuido, PaddleOCR-backend and minio servers.

version: "3.9"

x-logging:
  logging: &default-logging
    driver: "local"
    options:
      max-size: "10m"
      max-file: "3"

services:
  label-studio:
    container_name: label-studio
    image: heartexlabs/label-studio:latest
    restart: unless-stopped
    ports:
      - "8080:8080"
    depends_on:
      - minio
    environment:
      - JSON_LOG=1
      - LOG_LEVEL=DEBUG
    volumes:
      - label-studio-data:/label-studio/data

  # not replicated setup for test setup, use a proper aws S3 compatible cluster in production
  minio:
    container_name: minio
    image: bitnami/minio:latest
    restart: unless-stopped
    logging: *default-logging
    ports:
      - "9000:9000"
      - "9002:9001"
    volumes:
      - minio-data:/data
      - minio-certs:/certs
    # configure env vars in .env file or your systems environment
    environment:
      - MINIO_ROOT_USER=${MINIO_ROOT_USER:-minio_admin_do_not_use_in_production}
      - MINIO_ROOT_PASSWORD=${MINIO_ROOT_PASSWORD:-minio_admin_do_not_use_in_production}
      - MINIO_PROMETHEUS_AUTH_TYPE=${MINIO_PROMETHEUS_AUTH_TYPE:-public}
  paddleocr-backend:
    container_name: paddleocr-backend
    image: blazordevlab/paddleocr-backend:latest
    environment:
      - LABEL_STUDIO_HOST=${LABEL_STUDIO_HOST:-http://label-studio:8080}
      - LABEL_STUDIO_ACCESS_TOKEN=${LABEL_STUDIO_ACCESS_TOKEN}
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
      - AWS_ENDPOINT=${AWS_ENDPOINT:-http://minio:9000}
      - MINIO_ROOT_USER=${MINIO_ROOT_USER:-minio_admin_do_not_use_in_production}
      - MINIO_ROOT_PASSWORD=${MINIO_ROOT_PASSWORD:-minio_admin_do_not_use_in_production}
      - MINIO_API_CORS_ALLOW_ORIGIN=${MINIO_API_CORS_ALLOW_ORIGIN:-*}
      - OCR_LANGUAGE=${OCR_LANGUAGE:-ch}
    ports:
      - 9090:9090
    volumes:
      - paddleocr-backend-data:/data
      - paddleocr-backend-logs:/tmp
volumes:
  label-studio-data:
  minio-data:
  minio-certs:
  paddleocr-backend-data:
  paddleocr-backend-logs:

1. Upload tasks.

   If you're using the Label Studio Local File Storage option, upload images directly to Label Studio using the Label Studio interface.

   If you're using minio for task storage, log into the minio control panel at http://localhost:9001. Create a new bucket, making a note of the name, and upload your tasks to minio. Set the visibility of the tasks to be public. Furtner configuration of your cloud storage is beyond the scope of this tutorial, and you will want to configure your storage according to your particular needs.

2. If using minio, In the project Settings, set up the Cloud storage.

   Add your source S3 storage by connecting to the S3 Endpoint http://host.docker.internal:9000, using the bucket name from the previous step, and Access Key ID and Secret Access Key as configured in the previous steps. For the minio example, uncheck Use pre-signed URLS. Check the connection and save the storage.

3. Open the Machine Learning settings and click Add Model.

   Add the URL http://host.docker.internal:9090 and save the model as an ML backend.

4. To use this functionality, activate Auto-Annotation and use Autotdetect rectangle for drawing boxes

Example below :

Reference links :

• https://labelstud.io/blog/Improve-OCR-quality-with-Tesseract-and-Label-Studio.html⁠
• https://labelstud.io/blog/release-130.html⁠