OCR Text Extraction Using Streamlit

Overview

A powerful Streamlit application that uses Optical Character Recognition (OCR) to extract text from images and PDF files. The app employs advanced image processing techniques to enhance OCR accuracy and provide a user-friendly text extraction experience.

Key Features

• Multi-file upload support (PNG, JPG, JPEG, PDF)
• Advanced image preprocessing techniques
• Configurable OCR options
• Real-time text extraction
• Downloadable extracted text files

Technologies

• Python 3.7+
• Streamlit
• OpenCV
• Pytesseract
• PyMuPDF
• NumPy
• Pillow

OCR Processing Techniques

Image Preprocessing

• Deskewing
• Binarization (Otsu's method)
• Noise removal
• Contrast enhancement
• Page segmentation mode selection

Pytesseract Configuration

• LSTM neural network mode
• Uniform text block assumption
• Interword space preservation

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ocr-text-extraction-app.git
   cd ocr-text-extraction-app

2. Install dependencies:

   pip install -r requirements.txt

3. Install Tesseract OCR:

   • Ubuntu: sudo apt-get install tesseract-ocr
   • macOS: brew install tesseract
   • Windows: Download from Tesseract GitHub

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Open http://localhost:8501 in your web browser

3. Upload images or PDF files

4. Configure OCR options in the sidebar

5. Download extracted text files

Deployment

Deploy using Streamlit Cloud:

1. Push code to GitHub
2. Connect Streamlit Cloud to your repository
3. Configure build settings

Contributing

Contributions are welcome! Please submit pull requests to the main repository.

License

MIT License - see LICENSE file for details.