Team 102 - ChengYu Hsu and Sri Vamsi Andavarapu

Kubernetes-Based Scalable Multi-Language Image Processing Service

This project is a cloud-based Optical Character Recognition (OCR) system designed to process images and extract text in multiple languages. It is built using a microservices architecture and deployed on Kubernetes. The system leverages React for the frontend, Flask for the backend, and Tesseract for OCR processing.

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Table of Contents

1. Features
2. Architecture
3. Prerequisites
4. Setup Instructions
   • 1. Clone the Repository
   • 2. Install Required Tools
   • 3. Build Docker Images
   • 4. Configure Google Cloud Storage
   • 5. Set Up Kubernetes Secrets
   • 6. Deploy the Application to Kubernetes
   • 7. Running Locally Using Minikube
5. Scaling and Monitoring
6. Troubleshooting
7. Contributing
8. License

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Features

• Multi-language OCR processing (English, Hindi, Chinese, and Spanish).
• Scalable architecture using Kubernetes.
• Frontend for image upload and text display.
• Google Cloud Storage for image management.
• MongoDB for result storage.
• RabbitMQ for asynchronous task handling.

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Architecture

1. Frontend:

   • Built with React.
   • Users can upload images and retrieve processed text.

2. Backend:

   • Flask-based API.
   • Manages image uploads, queues tasks, and fetches results.

3. Worker:

   • Python-based processing service.
   • Retrieves images from Google Cloud Storage, performs OCR, and stores results in MongoDB.

4. Database:

   • MongoDB stores extracted text and metadata.

5. Queue:

   • RabbitMQ handles asynchronous task distribution.

6. Storage:

   • Google Cloud Storage stores uploaded images.

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Prerequisites

1. Tools and Dependencies:

   • Docker
   • Kubernetes (Minikube for local setup)
   • Node.js (18+)
   • Python (3.10+)
   • Google Cloud SDK
   • MongoDB Community Edition
   • RabbitMQ

2. Environment Configuration:

   • Service account JSON for Google Cloud Storage.
   • Kubernetes cluster setup (local or cloud).

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Setup Instructions

1. Clone the Repository

git clone https://github.com/your-repo/ocr-system.git
cd ocr-system

2. Install Required Tools

Node.js

sudo apt update
sudo apt install -y nodejs npm

Python

Install Python and dependencies:

sudo apt update
sudo apt install -y python3 python3-pip
pip install flask pymongo pika google-cloud-storage pytesseract

Docker

Install Docker following the official guide.

Kubernetes and Minikube

Install Minikube:

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube

3. Build Docker Images

Option 1: Using Docker Commands

Frontend

docker build -t ocr-frontend ./frontend

Backend

docker build -t ocr-backend ./backend

Worker

docker build -t ocr-worker ./worker

Push Images (if using a container registry)

docker tag ocr-backend gcr.io/<project-id>/ocr-backend
docker push gcr.io/<project-id>/ocr-backend

Option 2: Using Makefile

If your project includes a Makefile, you can use the following commands to build and push the images:

make build
make push

4. Configure Google Cloud Storage

Create a Service Account

1. Navigate to Google Cloud Console.
2. Create a service account with "Storage Admin" permissions.
3. Download the service account key as a JSON file.

Create a Storage Bucket

gsutil mb -p <your-project-id> gs://<bucket-name>

5. Set Up Kubernetes Secrets

Create a secret for Google Cloud credentials

kubectl create secret generic google-credentials \
  --from-file=key.json=/path/to/your-service-account-key.json

Verify the secret

kubectl get secrets

6. Deploy the Application to Kubernetes

Apply the manifests

kubectl apply -f k8s/mongodb-deployment.yaml
kubectl apply -f k8s/rabbitmq-deployment.yaml
kubectl apply -f k8s/backend-deployment.yaml
kubectl apply -f k8s/worker-deployment.yaml
kubectl apply -f k8s/frontend-deployment.yaml

Verify deployments

kubectl get pods
kubectl get services

7. Running Locally Using Minikube

Start Minikube

minikube start

Access the Frontend

minikube service ocr-frontend

Forward Ports (Optional)

kubectl port-forward service/ocr-frontend 8080:80

Scaling and Monitoring

Scale Worker Pods

kubectl scale deployment ocr-worker --replicas=3

Monitor Logs

kubectl logs <pod-name>

Enable Autoscaling

kubectl autoscale deployment ocr-worker --cpu-percent=50 --min=1 --max=5

Troubleshooting

Pods Not Running

Check pod status:

kubectl get pods

View logs:

kubectl logs <pod-name>

External IP Pending

Use Minikube service:

minikube service ocr-frontend

CSS Not Loading

Verify frontend build:

npm run build

RabbitMQ Connection Errors

Check RabbitMQ logs:

kubectl logs <rabbitmq-pod-name>

Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit changes:

   git commit -m "Added feature-name"

4. Push changes and open a pull request.

License

This project is licensed under the MIT License. See LICENSE for details.