How to Convert a Scanned PDF to Text: OCR Methods Compared

If you have a scanned PDF — a document that is essentially an image of text — you cannot simply copy and paste from it. The text is locked inside the image. To extract it, you need Optical Character Recognition (OCR) technology that recognises the letters and converts them into machine-readable text.

This guide covers the most common approaches for converting scanned PDFs to text, from free online tools to developer-friendly APIs, so you can choose the right method for your needs.

What Is a Scanned PDF and Why Is It Different?

A scanned PDF is created by running a physical document through a scanner or taking a photo of it. The resulting PDF contains an image of each page, not selectable text. This is fundamentally different from a digitally created PDF (like one exported from Word or Google Docs), where the text is already embedded in the file.

To extract text from a scanned PDF, you need OCR software that:

• Analyses the image for patterns that look like characters
• Matches those patterns against known letter shapes
• Reconstructs the text in a digital, editable format

OCR accuracy depends on the quality of the scan, the clarity of the original document, and the sophistication of the OCR engine.

Method 1: Free Online OCR Tools

For occasional use, free online OCR tools are the quickest option. You upload your scanned PDF, the tool processes it, and you download the extracted text.

How to use Google Docs for OCR:

1. Upload the scanned PDF to Google Drive
2. Right-click the file and select "Open with → Google Docs"
3. Google Docs automatically runs OCR and opens the document with extracted text
4. Copy the text or download as a .docx or .txt file

Other free online tools like OnlineOCR.net and OCR2Edit offer similar functionality with page limits. These work well for clean, high-contrast printed documents but may struggle with handwriting, unusual fonts, or low-quality scans.

Method 2: Desktop OCR Software

Adobe Acrobat Pro includes built-in OCR functionality. It can convert scanned PDFs to searchable and editable text, supports multiple languages, and can output to Word, Excel, or plain text.

Tesseract OCR is an open-source OCR engine originally developed by HP and now maintained by Google (GitHub: tesseract-ocr/tesseract). It supports over 100 languages (Tesseract documentation) and can be used through GUI frontends like OCRFeeder or gImageReader. Tesseract works well with preprocessed images — clean, deskewed, and high-contrast input produces the best results.

Method 3: Cloud OCR APIs for Developers

For developers who need to integrate OCR into their own applications, cloud-based OCR APIs handle image preprocessing, OCR, and text reconstruction, and return structured results.

Available cloud OCR APIs include:

• gettxt.ai — extracts text from PDFs, images, audio, and video through a single API (gettxt.ai API documentation)
• Google Cloud Vision — supports OCR on images and PDFs (Google Cloud Vision OCR docs)
• AWS Textract — includes table and form extraction from scanned documents (AWS Textract docs)
• Azure AI Document Intelligence — offers pre-built document models (Azure AI Document Intelligence)
• Tesseract (self-hosted) — free, open-source version available on GitHub

Why Use a Cloud API?

• Automation: Process hundreds or thousands of documents programmatically
• Integration: Results flow directly into your database, CMS, or workflow
• Multi-format: Some APIs like gettxt.ai handle PDFs, images, audio, and video through a single endpoint

Method 4: OCR with Python

Python developers have several options for programmatic OCR:

pytesseract (Tesseract wrapper)

import pytesseract
from PIL import Image

text = pytesseract.image_to_string(Image.open("scanned_page.png"))
print(text)

This approach requires Tesseract installed locally and works best with preprocessed images. The pytesseract library is available on PyPI (pytesseract on PyPI).

Cloud API client example

import requests

response = requests.post(
    "https://api.gettxt.ai/extract",
    files={"file": open("scanned.pdf", "rb")},
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)
print(response.json()["text"])

Tips for Better OCR Results

Regardless of which tool or API you choose, these steps improve OCR accuracy:

1. Scan at 300 DPI or higher — lower resolution loses character detail
2. Use black-and-white or grayscale — colour adds noise without benefit
3. Deskew the image — tilted text reduces OCR accuracy
4. Increase contrast — clear separation between text and background helps the OCR engine
5. Choose the right format — output to plain text for most use cases, or Markdown if you need structure

How to Choose the Right Method

Conclusion

Converting scanned PDFs to text is straightforward once you know the available options. Free online tools like Google Docs handle occasional one-off documents. Desktop software like Tesseract gives you more control for regular use. And for developers and businesses who need automated, programmatic text extraction, a cloud OCR API like gettxt.ai provides a scalable solution that works with PDFs, images, audio, and video through a single integration.

Start with the simplest approach that meets your needs, and explore more advanced options as your requirements grow.