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README.md

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 short_description: This Model helps to extract text from inputted images.
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+# OCR Web Application
+
+This is a web application built using Gradio and Hugging Face Transformers for Optical Character Recognition (OCR). The application can extract text from images, support multiple languages, and highlight specified keywords.
+
+## Table of Contents
+- [Prerequisites](#prerequisites)
+- [Setup Instructions](#setup-instructions)
+- [Running the Application Locally](#running-the-application-locally)
+- [Deployment Process](#deployment-process)
+- [Model Description](#model-description)
+
+## Prerequisites
+
+Make sure you have the following installed on your machine:
+
+- Python 3.6 or higher
+- pip (Python package installer)
+
+## Setup Instructions
+
+1. **Clone the Repository**:
+
+   Clone this repository to your local machine using the following command:
+
+   git clone <repository-url>
+   cd <repository-name>
+
+
+Create a Virtual Environment (Optional but Recommended):
+
+It is recommended to create a virtual environment to manage your dependencies.
+
+python -m venv ocr-env
+source ocr-env/bin/activate  # On Windows use: ocr-env\Scripts\activate
+
+
+Install Required Libraries:
+
+Use the following command to install the required libraries:
+
+pip install -r requirements.txt
+
+Running the Application Locally
+
+To run the web application locally, execute the following command in your terminal:
+
+python app.py
+
+
+
+##Deployment Process
+Once I was satisfied with the functionality of my web application, I decided to deploy it to make it accessible to others. Here’s how I did it:
+
+Choose a Deployment Platform: I opted for Hugging Face Spaces because it allows easy deployment for machine learning applications. However, other platforms like Streamlit Sharing or Heroku could also work.
+
+Clone the Repository: First, I cloned my project repository from GitHub or any other version control platform I was using. This ensured I had all the latest code on my local machine.
+
+bash
+Copy code
+git clone https://github.com/username/OCR_Model.git
+Set Up Environment Variables: If my application required any sensitive information or API keys, I made sure to set those up in environment variables on the deployment platform.
+
+Requirements File: I created a requirements.txt file that listed all the necessary libraries:
+
+plaintext
+Copy code
+gradio
+transformers
+Pillow
+requests
+torch
+tensorflow
+tf-keras
+This file would ensure that the platform installs all the dependencies needed to run the application.
+
+Deployment Configuration: On Hugging Face Spaces, I navigated to the "Create a Space" option and selected the "Gradio" template. I uploaded my code and the requirements.txt file to the space. The platform automatically detects the required libraries and installs them.
+
+Running the Application: After the upload, I clicked on the "Run" button. The Hugging Face platform handles the execution of my application. I could see real-time logs, which helped in debugging if anything went wrong during the startup process.
+
+Testing: Once the application was running, I accessed the URL provided by Hugging Face to test its functionality. I made sure everything was working as expected before sharing it with others.
+
+Sharing: After confirming that the application was live and functional, I shared the link with friends, colleagues, and any potential users to gather feedback and improve the application.
+
+By following these steps, I successfully deployed my web application, making it accessible for anyone interested in using my OCR model.
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference