OCR: How to Extract Text from Images, Videos, and PDFs (2026 Guide)

I have one rule: don’t retype what you can copy.

But the web is full of “uncopyable” text:

• text inside images
• text inside YouTube/course video frames
• sites that block selection/right-click
• scanned PDFs (you can see it, but you can’t select it)

That’s what OCR (Optical Character Recognition) is for: turning what you see on screen into copyable text.

This guide explains when to use OCR, how to get better accuracy, and how to handle tables/code—using Eyesme Extension as the example workflow.

When to use OCR (and when not to)

Before you “OCR everything,” decide your goal:

• Copy a paragraph/quote → OCR (plain text)
• Convert a table into editable data → table extraction (CSV/JSON)
• Extract runnable code → code extraction (keep indentation)
• Understand meaning (charts/UI/clauses/errors) → screenshot analysis

Related:

• Smart Screenshot Analysis (2026 Guide)
• Extract Data from Tables in PDFs and Screenshots
• Extract Code from Screenshots (Developer OCR Guide)

OCR workflow with Eyesme (5 steps)

Step 1: Capture the smallest useful area

OCR hates noise. Crop tightly to the text you need.

Step 2: Clarity beats “better models”

• if the font is small, zoom in and re-capture
• if the frame is blurry, pause on a clearer moment
• avoid low-contrast text (light gray on gray)

Step 3: Specify the output format

Same image, different useful formats:

• plain text (paragraphs/quotes)
• line-by-line list (steps/items)
• key-value pairs (serial numbers/config fields)

Step 4: Quick validation (10 seconds)

Check common confusions:

• 0/O, 1/I/l
• missing dots/slashes in URLs/emails
• commas/decimal points in numbers

Step 5: If you need meaning, follow up

OCR gives you text. Then ask for understanding:

• “Summarize this in 3 bullets.”
• “Explain the key terms with examples.”
• “Convert this config into a runnable command.”