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The project creates the models and service API for predicting scanned document images' angles ranging between -90° to 90° from the vertical.

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skconan/Scanned-Document-Rotation-Correction

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Scanned Document Rotation Correction

The project creates the models and service API for predicting scanned document images' angles ranging between -90° (counter clockwise) to 90° (clockwise) from the vertical.

Setup Environment For Model Training and Testing

conda env create -f conda_env.yml -p <path of env>

conda activate <env_name> 

pip install -r requirements.txt

Training

  1. Dataset preparation

    • Convert PDF documents to images.

    • Download scanned document images from website.

    • Etc.

  2. Data augmentation (Coming soon)

     python src/data_augmentation.py
    
  3. Save dataset to Torch format (.pt) (Coming soon)

     python src/create_dataset.py
    
  4. Training

    4.1 Edit dataset path

    4.2 Training

     python src/training.py
    

    4.3 TensorBoard Running

     tensorboard --logdir=<log dir> --port <port>
     
     tensorboard --logdir=runs --port 8000
    

Models

Download rotation_net.onnx into models/

Running Service API with docker

  1. Start up application by running.

     docker-compose up
    

    Or

     docker-compose -f .\docker-compose.yml up
    
  2. Open http://127.0.0.1:9000/docs API documentation.

  3. Send POST Request to http://127.0.0.1:9000/angle or api_endpoint/angle with Json format.

     {
       "image" : "image_base_64"
     } 
    
  4. Response is angle in range from -90° to 90° (counter clockwise to clockwise).

     {
         "angle": 0.7157974243164062
     }
    

Testing Output

Success Cases Failed Cases

Todo list

  • Add data_augmentation script

  • Add create dataset script

  • Impove model accuracy

Contributing

Anyone's participation is welcome! Open an issue or submit PRs.

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The project creates the models and service API for predicting scanned document images' angles ranging between -90° to 90° from the vertical.

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