PDF OCR API: Turn Scanned PDFs into Searchable Text

What it does

A scanned PDF is just a stack of pictures. Search boxes, LLMs and full-text indexes see nothing inside it until each page has been run through OCR. The PDF OCR actor does that for you in the cloud: paste links to archive scans, faxes, signed contracts, invoices or photocopied letters, and it returns the text of each page with line and paragraph breaks kept.

Many real PDFs are mixed: a digital report with a few scanned appendix pages, or a contract where only the signature pages were scanned. In the default auto mode the actor reads pages that already have a text layer directly (exact, and charged at $0.30 per 1,000 pages) and only OCRs the pages that are images (charged at $3 per 1,000 pages). You do not pay OCR prices for text that was already there.

Recognition runs on Tesseract 5 with its LSTM models in 29 languages, up to 4 per run. Every OCR'd page carries a confidence score, so you can spot the pages worth a human look, and optional word-level bounding boxes help with redaction and highlighting. It suits records teams digitising archives, developers adding a PDF OCR step to an app, and anyone feeding paper documents into a RAG pipeline.

How it works

1. You pass one or more PDF links in urls (images such as PNG, JPEG and TIFF are accepted too).
2. Each PDF is opened page by page; in auto mode, pages with an existing text layer are read directly and the rest are rendered at the chosen dpi (300 by default).
3. Rendered pages are recognised by Tesseract 5 in the languages you selected, with an optional page segmentation mode for forms or receipts.
4. Results are written as one row per page (the default for this actor) or one row per PDF, with text, word count, OCR confidence and page counts by method.
5. Failed downloads, unreadable files and blank pages are reported but not charged, and the run stops cleanly at your maximum total charge.

Input example

A typical run input (JSON). Every field is also available as a form in the Apify console.

{
  "urls": [
    "https://raw.githubusercontent.com/ocrmypdf/OCRmyPDF/main/tests/resources/linn.pdf",
    "https://arxiv.org/pdf/1706.03762"
  ],
  "languages": [
    "eng"
  ],
  "pdfMode": "auto",
  "outputMode": "pages",
  "dpi": 300,
  "maxPagesPerPdf": 200
}

Output example

Below, a scanned brochure is OCR'd (one OCR page, 95.5 confidence) while a digital paper is read from its text layer with no OCR at all.

Pricing

Pay per event: you are charged only for the results you receive, with no subscription. Set a maximum total charge on any run and it stops cleanly at the cap.

Prices are read from the live Apify Store listing when this page is built. The listing is always authoritative.

Use cases

How it compares

If you already run Tesseract on your own servers, or your documents must never leave your network, self-hosting is the better fit. This actor is for teams who want the same engine without maintaining it, with text-layer detection and billing per page built in.

Limits and honest notes

• Tesseract reads printed and typed text very well (95%+ confidence on clean scans); handwriting, low-resolution photos, heavy stamps and decorative fonts come out poorly. The confidence score tells you which pages to check.
• Output is plain text in reading order with line and paragraph breaks. Tables come out as lines of text, not structured tables. Images and charts have no text to extract.
• Pages rotated by 90° or 180° are not auto-rotated; slightly skewed scans are fine.
• Up to 4 languages per run; picking only the languages your documents use is faster and more accurate. Modern Greek is not available.
• HEIC/AVIF photos and SVG are not supported (convert photos to JPEG). Files up to 100 MB; PDFs up to 2,000 pages each (200 by default).
• Password-protected PDFs need the password input. Links that return a web page instead of a file are reported and not charged.
• Respects your run budget: set a maximum total charge and processing stops cleanly at the cap, with no page charged twice.

FAQ

Use it from code

Run the actor through the Apify API with the input above.

cURL

curl -X POST "https://api.apify.com/v2/acts/actor-forge~pdf-ocr/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"urls": ["https://raw.githubusercontent.com/ocrmypdf/OCRmyPDF/main/tests/resources/linn.pdf", "https://arxiv.org/pdf/1706.03762"], "languages": ["eng"], "pdfMode": "auto", "outputMode": "pages", "dpi": 300, "maxPagesPerPdf": 200}'

JavaScript (Node.js)

// Node.js 18+ (built-in fetch). Set APIFY_TOKEN in your environment.
const input = {
  "urls": [
    "https://raw.githubusercontent.com/ocrmypdf/OCRmyPDF/main/tests/resources/linn.pdf",
    "https://arxiv.org/pdf/1706.03762"
  ],
  "languages": [
    "eng"
  ],
  "pdfMode": "auto",
  "outputMode": "pages",
  "dpi": 300,
  "maxPagesPerPdf": 200
};

const res = await fetch(
  "https://api.apify.com/v2/acts/actor-forge~pdf-ocr/run-sync-get-dataset-items",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.APIFY_TOKEN}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify(input),
  }
);
const items = await res.json();
console.log(items.length, items[0]);

Python

# pip install apify-client   (set APIFY_TOKEN in your environment)
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = {
    "urls": [
        "https://raw.githubusercontent.com/ocrmypdf/OCRmyPDF/main/tests/resources/linn.pdf",
        "https://arxiv.org/pdf/1706.03762"
    ],
    "languages": [
        "eng"
    ],
    "pdfMode": "auto",
    "outputMode": "pages",
    "dpi": 300,
    "maxPagesPerPdf": 200
}

run = client.actor("actor-forge/pdf-ocr").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

The run-sync-get-dataset-items endpoint waits for the run (up to 5 minutes) and returns the dataset rows directly. For longer runs, start the run asynchronously and read the dataset when it finishes, as the Python example does. Replace APIFY_TOKEN with your own token from the Apify console.

Step-by-step guides

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