Responsive Doc. scanner using U^2-Net, Textcleaner and Tesseract

Overview

Responsive Doc. scanner using U^2-Net, Textcleaner and Tesseract

Toolset

  • U^2-Net is used for background removal
  • Textcleaner is used for image cleaning and line deskew (max 5 degrees)
  • Tesseract is used for text angle rotation
  • Deskew is used for line deskew (between 5 and 45 degrees)

Examples

Tested one document on smartphone camera with different angles


To build & deploy

  1. Clone thee repo
  2. Download the model: check app/saved_models/README.md
  3. Build Docker image : docker build -t / : .
  4. Test locally : Run Docker image and check if api is working by running http://localhost:10000
    • CPU : docker run -it -v $PWD:/LOCAL/ -p 10000:80 / :
    • GPU : docker run -it --gpus all -v $PWD:/LOCAL/ -p 10000:80 / :
  5. Push docker image to Dockerhub (optional):
  6. Deploy to Cloud Run (optional):
    • Create your google cloud account
    • Push Docker Image to Google Container Registry
      • create new project called [PROJECT-ID]
      • Open Cloud shell in your Google account and run: docker pull / : docker tag [IMAGE] gcr.io/[PROJECT-ID]/[IMAGE] docker push gcr.io/[PROJECT-ID]/[IMAGE] more detail in this link
    • Create CloudRun Service, and select Container that was created
      • Screenshot of the config - for demo purpose, it will be cost free
    • Click Deploy, and test the Api Url that will display

Limits and Areas for improvements

  • Speed: It takes 7 to 10 seconds to process one image (serverless Cloud Run) With Gpu we can save 2 to 3 seconds (U^2-Net is 3 times faster)
  • Textcleaner is slow but works better on image cleaning, but needs some manual fine-tuning

References

Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:

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