README.md

---
title: GOT OCR Web App  # Title of your app
emoji: 📄  # You can choose any emoji that represents your app
colorFrom: blue  # Start color for the gradient background
colorTo: green  # End color for the gradient background
sdk: streamlit  # Your app uses Streamlit
sdk_version: "1.21.0"  # Version of Streamlit you are using
app_file: app.py  # Entry point of your application
pinned: false  # Whether this Space should be pinned on your profile
---



# OCR Web Application

## Project Overview
This is a **web-based Optical Character Recognition (OCR) application** built using Streamlit. The app supports both English and Hindi languages, allowing users to upload images and extract text using advanced OCR models.

## Live Demo
You can access the live demo of the application at: [https://huggingface.co/spaces/Trisandhya/GOT-OCR-WEB-APP](https://huggingface.co/spaces/Trisandhya/GOT-OCR-WEB-APP)

## How the Application Works
1. Choose Language: Select either English or Hindi using the sidebar instructions.
2. Upload Image: Use the file uploader to input an image in JPG, PNG, or JPEG format.
3. Text Extraction: For English, the app uses the GOT OCR 2.0 model to extract text, while for Hindi, it leverages EasyOCR.
4. Keyword Search: After text extraction, you can search for specific keywords within the extracted text. Matching keywords will be highlighted, and any missing keywords will be displayed in a warning message.
5. Reset: If needed, reset the session and upload a new image to start over.

## Installation and Setup

### Prerequisites:
- **Python 3.8 or higher**
- Required libraries listed in `requirements.txt`

### Installation Steps:
1. **Clone the repository**:
   ```bash
   git clone https://github.com/Trisandhyadevi/OCR.git

2. **Navigate to the project directory**
   ```bash
    cd OCR

3. **Install the required dependencies:**
    ```bash
    pip install -r requirements.txt

4. **Run the application:**
    ```bash
    streamlit run app.py


# Description

This web application facilitates the conversion of images to text using the GOT OCR 2.0 Model for English text extraction. While the GOT model excels in processing English content, fine-tuning it on a Hindi dataset is not feasible on a CPU. Therefore, for Hindi text extraction, we utilize EasyOCR, which provides effective performance for this language.

# GOT OCR 2.0 Model Overview

The GOT OCR 2.0 Model is a state-of-the-art OCR system designed for accurate text extraction from images. Key features include:

- **Multi-task Learning**: The model supports various tasks beyond OCR, including layout analysis and object detection, making it versatile for diverse text recognition needs.
- **End-to-End Pipeline**: It efficiently processes entire images, identifying and extracting text without the need for additional preprocessing steps.

Note: Currently, the model does not support all languages. Fine-tuning is required for languages not included in the pre-trained model. For more information on fine-tuning, visit the [GOT OCR 2.0 Fine-tuning Guide](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/?tab=readme-ov-file#fine-tune).

For more technical details about the model architecture and usage, visit the [GOT OCR 2.0 Model Documentation](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/?tab=readme-ov-file#general-ocr-theory-towards-ocr-20-via-a-unified-end-to-end-model).

## Folder Structure

<pre>
.
├── app.py                
├── requirements.txt       
└── README.md      

</pre>

## Deployment

# To deploy the application to a  Hugging Face cloud platform 

1. Use GitHub Actions: Set up GitHub Actions in your repository to automate the deployment process to Hugging Face Spaces.

2. Follow Documentation: For detailed instructions on setting up GitHub Actions for Hugging Face Spaces, refer to the [Hugging Face Spaces GitHub Actions Documentation](https://huggingface.co/docs/hub/spaces-github-actions).


## Contact
For any further queries or assistance, feel free to reach out via email at:[[email protected]]([email protected])