README.md

metadata

title: GOT OCR Web App
emoji: 📄
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.21.0
app_file: app.py
pinned: false

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.

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:

   git clone https://github.com/Trisandhyadevi/OCR.git
   

2. Navigate to the project directory

    cd OCR
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Run the application:

   streamlit run app.py
   

Description

This web application supports converting images to text using the GOT OCR 2.0 Model. Below are some key features of the GOT OCR 2.0 model

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.

For more technical details about the model architecture and usage, visit the GOT OCR 2.0 Model Documentation.

Deployment

To deploy the application to a cloud platform(Hugging Face)

Folder Structure

.
├── app.py                # Main application file
├── requirements.txt       # Python dependencies
└── README.md              # Projectdocumentation