OCR and Document Search Web Application

This project is a web-based prototype that performs Optical Character Recognition (OCR) on uploaded images with text in both Hindi and English. It also features a keyword search within the extracted text.

Features

• Upload an image containing text in Hindi and/or English.
• Extract text from the image using OCR.
• Perform keyword search within the extracted text.
• Web app interface built using Streamlit.

Prerequisites

Before running the project, ensure you have the following installed on your system:

• Python 3.7 or above
• pip (Python package installer)
• (Optional) Virtual environment tool (virtualenv or venv)

Setting Up the Environment

Step 1: Clone the Repository

First, clone the project repository to your local machine:

git clone <REPO_URL>
cd ocr-document-search

Setting Up the Environment

Step 2: Create a Virtual Environment (Recommended)

It's recommended to use a virtual environment to avoid conflicts with system-wide packages. Follow the instructions based on your operating system:

Windows

python -m venv ocr-app-env
ocr-app-env\Scripts\activate

macOS/Linux

python3 -m venv ocr-app-env
source ocr-app-env/bin/activate

Step 3: Install Dependencies

Once your virtual environment is activated, install the required dependencies by running:

pip install -r requirements.txt

The requirements.txt file should contain:

streamlit
easyocr
torch
transformers
Pillow

If you run into issues with installing torch, refer to the official PyTorch installation guide.

Running the Web Application Locally

Step 1: Run the Streamlit Application

After installing the dependencies, you can run the application using Streamlit:

streamlit run app.py

This will launch a local development server. You can access the application by navigating to http://localhost:8501 in your web browser.

Step 2: Using the Application

• Go to the local URL (http://localhost:8501).
• Upload an image containing text in Hindi and/or English.
• The OCR will extract text from the image and display it.
• Use the search functionality to find specific keywords in the extracted text.

Deployment Process

To make the web application accessible online, you can deploy it using a cloud service like Streamlit Sharing or a platform of your choice. Here’s how you can do it:

Step 1: Push the Code to GitHub

Ensure your project is pushed to a GitHub repository. If you haven't already, create a new GitHub repository and upload your project files.

git init
git remote add origin <GITHUB_REPO_URL>
git add .
git commit -m "Initial commit"
git push -u origin main

Step 2: Deploy on Streamlit Sharing

• Visit Streamlit Sharing.
• Log in with your GitHub account.
• Select the repository containing your project.
• Choose the app.py file as the entry point.
• Streamlit will automatically deploy the application and provide you with a public URL.

Project Structure

ocr-document-search/
│
├── app.py                    # Main Streamlit app file
├── requirements.txt           # List of dependencies
└── README.md                  # Project documentation

Additional Notes

• This application uses the easyocr library to perform OCR, which supports both Hindi and English text.
• The web interface is built using Streamlit, which provides a fast and easy way to create web applications with Python.
• The application is designed to be lightweight and simple, demonstrating the core functionality of OCR and keyword search.