PDF Text Recognition Online

Worked example

Example: run text recognition on multi-page handwritten notes scan—quality depends on DPI; proofread before archiving.

When to use this

• Extracting quotes from scanned articles.
• Building searchable archives from image PDFs.
• Preparing scans for data entry workflows.

Common examples

• Phone photo scan of a receipt → searchable text export where you can copy line items after proofreading totals.
• Old faxed letter PDF → text extracted so keyword search works in document management systems.
• Multi-page handwritten notes scan → OCR quality depends on scan DPI; proofread before archiving.
• Five-page typed scan at 300 DPI → searchable PDF for keyword find in Preview.
• Historical newspaper clip scan → expect errors on faded serif fonts; proofread names.

Common mistakes

• Trusting OCR on smudged or faint text without review.
• Using recognition on password-locked PDFs without unlocking.
• Expecting table structure to survive OCR perfectly.

How it works

Upload a scanned or image-based PDF and extract readable text on your device. Each page is processed locally with browser OCR, recognized line by line, and combined into a downloadable .txt file. Text recognition accuracy depends on scan resolution, contrast, skew, and fonts—handwriting and low-DPI phone photos often need cleanup.

Limitations

Text recognition accuracy depends on scan quality, fonts, and language. Review extracted text before relying on it for legal, medical, or financial decisions.

Privacy and file handling

Your data stays on your device and is not uploaded.

Formula or method

OCR detects text regions per page, recognizes characters with a local engine, and outputs plain Unicode text in reading order. Multi-column layouts may interleave columns; split pages with Extract PDF Pages when structure matters. Output is plain text—not a reflowed PDF with a hidden text layer.