OCR API for Bulk Scanned PDFs and Images in One Run

What it does

Document intake is rarely tidy. One supplier emails a scanned PDF, another sends a phone photo of the same invoice, the fax gateway produces TIFFs and a support form uploads screenshots. Building a separate path for each format is busywork. The OCR to Text actor takes all of them in one urls list and returns text in a consistent row format.

It accepts PDF, PNG, JPEG, TIFF, WebP, GIF, BMP and JPEG 2000. Each image counts as one page; each PDF is read page by page, with pages that already have a text layer read directly at a tenth of the OCR price. By default you get one row per file with all its text, which keeps downstream code simple whatever the input type.

Under the hood it runs Tesseract 5 with LSTM models in 29 languages, up to 4 per run. That makes it a good fit for automation work: back-office pipelines, AI agents that need a reliable OCR tool, and scheduled batch jobs where a run budget cap matters. Failed downloads, unreadable files and blank pages are never charged.

How it works

1. You submit a list of file links in urls, mixing PDFs and image formats freely, and optionally cap the batch with maxFiles.
2. The actor detects each file's type; images go straight to OCR, PDFs are split into pages and text-layer pages are read without OCR in auto mode.
3. Tesseract 5 recognises the image pages in your chosen languages, dropping low-confidence words if you set minWordConfidence.
4. Each file becomes one dataset row by default (outputMode document), or one row per PDF page if you switch to pages.
5. Charges are per page read, and the run stops cleanly when it reaches the maximum total charge you set.

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://raw.githubusercontent.com/tesseract-ocr/test/main/testing/phototest.tif",
    "https://github.com/tesseract-ocr/tessdoc/raw/main/images/eurotext.png"
  ],
  "languages": [
    "eng",
    "fra"
  ],
  "pdfMode": "auto",
  "outputMode": "document",
  "minWordConfidence": 40,
  "maxFiles": 5000
}

Output example

The sample shows a scanned PDF, a TIFF test image and a digital PDF processed in the same run, each as one row with its file type and page counts.

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 only ever process one kind of file, a dedicated tool such as PDF OCR or Image to Text OCR gives you the same engine with defaults tuned for that input. Teams with strict data residency needs may still prefer a self-hosted OCR service.

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~ocr-to-text/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://raw.githubusercontent.com/tesseract-ocr/test/main/testing/phototest.tif", "https://github.com/tesseract-ocr/tessdoc/raw/main/images/eurotext.png"], "languages": ["eng", "fra"], "pdfMode": "auto", "outputMode": "document", "minWordConfidence": 40, "maxFiles": 5000}'

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://raw.githubusercontent.com/tesseract-ocr/test/main/testing/phototest.tif",
    "https://github.com/tesseract-ocr/tessdoc/raw/main/images/eurotext.png"
  ],
  "languages": [
    "eng",
    "fra"
  ],
  "pdfMode": "auto",
  "outputMode": "document",
  "minWordConfidence": 40,
  "maxFiles": 5000
};

const res = await fetch(
  "https://api.apify.com/v2/acts/actor-forge~ocr-to-text/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://raw.githubusercontent.com/tesseract-ocr/test/main/testing/phototest.tif",
        "https://github.com/tesseract-ocr/tessdoc/raw/main/images/eurotext.png"
    ],
    "languages": [
        "eng",
        "fra"
    ],
    "pdfMode": "auto",
    "outputMode": "document",
    "minWordConfidence": 40,
    "maxFiles": 5000
}

run = client.actor("actor-forge/ocr-to-text").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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