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🧠 Overview: OCR Text Extraction

Optical Character Recognition (OCR) is a technology that converts printed, handwritten, or scanned text in images or documents into machine-readable text.

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⚙️ How It Works

1. Image Preprocessing

   • The input image is cleaned to improve text visibility.

   • Common steps:

     • Grayscale conversion
     • Noise removal
     • Binarization (convert to black and white)
     • Deskewing and resizing

2. Text Detection

   • The algorithm identifies regions of interest (ROI) that contain text.
   • Modern OCR systems use deep learning models (like EAST or CRAFT) to locate text areas accurately.

3. Character Recognition

   • Each detected text region is analyzed to recognize characters or words.
   • Traditional OCR uses pattern matching or feature extraction.
   • Modern systems use deep learning (CNNs, LSTMs, Transformers) for better accuracy.

4. Post-processing

   • Corrects errors and reconstructs structured text.
   • Includes spell-checking, language modeling, and format preservation (like paragraphs or tables).

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📄 Output

• Converts scanned or photographed documents (PDFs, images) into:

  • Editable text (TXT, DOCX, etc.)
  • Searchable PDFs
  • Extracted structured data (e.g., names, dates, numbers)

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⚙️ Common Tools and Libraries

• Tesseract OCR (open-source by Google)
• EasyOCR (deep learning–based)
• PaddleOCR, AWS Textract, Google Vision API, Azure OCR

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