This project contains the implementation of REST API empowered by Vision AI to detect the text available in the input image. The REST API services are being distributed through the docker image. You can pull the image from Docker Hub. The image comes as production-ready with unit tests and a standard algorithm implemented which can be used to expose the REST API to the web.
Following are the major contents to follow, you can jump to any section:

1. Run Locally
2. REST API with Docker
   
   • Pull Image From Docker Hub
     
   • Docker Container
     
     • Run a Docker Container
       
     • Perform Unit Tests
       
     • API documentation
       
   • Make an API Request
     

• Ensure you have Python 3.7+ installed.

• Create a new Python Conda environment:

conda create -n ocr python=3.10  # create ocr
conda activate ocr  # activate ocr

OR

• Create a new Python virtual environment with pip:

virtualenv ocr --python=python3.10  # create ocr
source ocr/bin/activate   # activate ocr

Install dependencies

  pip install -r requirements.txt

or

 conda env create -f environment.yaml

Clone the project

  git clone https://github.com/Hassi34/Optical-Character-Recognition.git

Go to the project directory

  cd Optical-Character-Recognition

Install dependencies

  pip install -r requirements.txt

Start the server

  uvicorn main:app --reload

To run the following sequence of commands, make sure you have the docker installed on your system.

In case you have not already pulled the image from the Docker Hub, you can use the following command:

docker pull hassi34/optical-character-recognition

Now once you have the docker image from the Docker Hub, you can now run the following commands to test and deploy the container to the web

• Run a Docker Container
  Check all the available images:

docker images

Use the following command to run a docker container on your system:

docker run --name <CONTAINER NAME> -p 80:80 -d <IMAGE NAME OR ID>

Check if the container is running:

docker ps

If the container is running, then the API services will be available on all the network interfaces
Type localhost in the browser and see if you get the success message from the API service.

• Perform Unit Tests
  After when the API services are up and running, run the following command on the terminal to perform the unit test:

docker exec -it <CONTAINER NAME OR ID> pytest

• API documentation
  The automatic API documentation will be available at http://localhost/docs

Use the following script as a reference to make a REST API request:

import requests
import base64

BASE64_STR = "" 
ENDPOINT = "http://127.0.0.1/predict"


def encodeImageIntoBase64(IN_IMG_PATH):
    with open(IN_IMG_PATH, "rb") as f:
        return base64.b64encode(f.read())

if __name__ == '__main__':
    response = requests.post(ENDPOINT, json={"base64_str":BASE64_STR})
    if response.status_code == 200:
        response = response.json()
        print(response)
    else :
        print(response)

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