🦙 OCR Vision App

OCR Vision App is a user-friendly tool designed to extract structured text from images using advanced OCR models. This application supports multiple OCR models, including EasyOCR, Llama3.2 Vision (local), and Groq's Llama3.2 Vision (via API). The app provides flexible output formats: plain text, Markdown, or JSON.

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Features

• OCR Model Options:

  • EasyOCR: Lightweight and quick.
  • Llama3.2 Vision: Advanced local model for high-accuracy OCR tasks.
  • Groq's Llama3.2 Vision API: State-of-the-art vision model via API integration.

• Flexible Output Formats:

  • Plain Text: For simple text extraction.
  • Markdown: For formatted content.
  • JSON: For structured output and metadata.

• Dynamic Image Uploads:

  • Supports PNG, JPG, and JPEG formats.
  • Compresses and resizes images for efficient processing.

• Downloadable Results:

  • Save your extracted text in your chosen format.

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Prerequisites

1. Install Dependencies

Install the required Python libraries from requirements.txt:

pip install -r requirements.txt
I'll help format this README section properly in Markdown:

```markdown
## Prerequisites

### 1. Install Dependencies
Install the required Python libraries from `requirements.txt`:

```bash
pip install -r requirements.txt

2. Set Up API Key for Groq's Llama Model

To use Groq's Llama3.2 Vision model via API:

1. Create a .streamlit folder in your project root directory
2. Inside .streamlit, create a .secrets.toml file

Here's a sample .secrets.toml structure:

# .streamlit/secrets.toml
GROQ_API_KEY = "your_groq_api_key_here"

A template .secrets_example.toml is provided in the repository to guide you.

3. Download Llama Model Locally

To run Llama3.2 Vision locally:

1. Visit Llama model hub to download the required model
2. Place the downloaded model in a directory accessible to the app, e.g., models/llama3.2

Getting Started

1. Clone the Repository

   git clone https://github.com/obinopaul/OCR_vision-app.git
   cd OCR_vision-app

2. Set Up Environment

   • Install dependencies using requirements.txt
   • Add your API keys to .streamlit/secrets.toml

3. Run the Application

   streamlit run app.py

Usage

1. Upload an Image

   • Choose an image (PNG, JPG, or JPEG)
   • The app resizes the image for optimal processing

2. Select an OCR Model

   • EasyOCR for lightweight tasks
   • Llama3.2 Vision (local) for robust accuracy
   • Groq's Llama3.2 Vision API for cutting-edge OCR via API

3. Choose an Output Format

   • Plain text, Markdown, or JSON

4. Extract Text

   • Click the "Extract Text 🔍" button to perform OCR

5. Download Results

   • Save the extracted text in your preferred format

Output Formats

• Plain Text

  • Simple text format
  • Filename: extracted_text.txt

• Markdown

  • Formatted content for documentation purposes
  • Filename: extracted_text.md

• JSON

  • Structured output with extracted text and metadata
  • Filename: extracted_text.json