4.1.3. Optical Character Recognition (OCR)

OCR extracts text from images. It converts visual text (in photos, scanned documents) into machine-readable text.

Key characteristics:

• Input: Image containing text
• Output: Extracted text strings with position information
• Works on printed and handwritten text

How OCR works:

1. Text detection: Find regions containing text in the image
2. Text recognition: Convert detected regions to character strings
3. Layout analysis: Understand reading order and structure
4. Output generation: Return text with bounding boxes

OCR output includes:

• Extracted text content
• Bounding box coordinates for each text region
• Confidence scores for recognition accuracy
• Reading order inference for multi-column layouts

Common scenarios:

• Digitizing paper documents
• Reading text from signs
• Extracting data from receipts
• Processing medical records
• Capturing business card information
• Accessibility applications (text-to-speech from images)

OCR vs Document Intelligence:

⚠️ Critical Exam Trap: OCR is a COMPUTER VISION workload, not NLP! The input is an image. The fact that the output is text doesn't change this—always classify by input type.