Image to Text OCR: Images & Scanned PDFs, 80+ Languages

Image to Text OCR extracts text from images and PDFs in bulk. Give it URLs of JPG, PNG, WebP, TIFF, GIF, BMP or AVIF images and PDF files. You get the text of each file with line breaks and paragraphs kept, a confidence score, and optionally every line with its bounding box. Scanned PDF pages are OCR'd; pages that already contain text are read exactly (no OCR errors, 7x cheaper). 80+ languages.

Try it now: press Start. The prefilled input reads 3 files (a scan, a receipt photo and a scanned PDF) in under a minute for about one cent. Price: $2 per 1,000 images, $0.30 per 1,000 text PDF pages. Failed files and images without text are free. Apify's free plan ($5 of monthly credit) covers about 2,400 images a month.

OCR runs on Tesseract 5's LSTM engine inside the actor. Your files go to no third-party OCR API and no Google service, and nothing is kept after the run except your own dataset.

Use cases

• Receipts and invoices: turn photos and scans into text for expense tools, bookkeeping or an LLM that extracts totals, dates and VAT (example and a ready n8n node below).
• Scanned PDFs and archives: make old letters, contracts, reports and books searchable; feed them to RAG pipelines.
• Screenshots: pull text out of app and website screenshots, error dialogs, slides, charts and memes.
• Product images and labels: read text from packaging, labels, menus and signs (use the Sparse text layout).
• Mixed document dumps: one run handles images, scanned PDFs and digital PDFs and charges each correctly.

How to use Image to Text OCR

1. Paste file URLs into Image or PDF URLs, one per line (direct links to the file, not to a web page that shows it). Base64 data: URLs work too.
2. Set Languages to the languages in the text (English is the default; each extra language slows OCR down).
3. Press Start. Each file becomes one dataset item with its text.
4. Export to JSON, CSV or Excel, or read the dataset with the API. For big batches give the run more memory (8 GB = 4 parallel OCR workers).

Input

{
  "urls": [
    "https://tesseract.projectnaptha.com/img/eng_bw.png",
    "https://upload.wikimedia.org/wikipedia/commons/0/0b/ReceiptSwiss.jpg",
    "https://raw.githubusercontent.com/ocrmypdf/OCRmyPDF/main/tests/resources/skew.pdf",
    "https://www.irs.gov/pub/irs-pdf/fw9.pdf"
  ],
  "languages": ["eng", "deu"]
}

Output

One item per file. A real result:

{
  "url": "https://raw.githubusercontent.com/naptha/tesseract.js/master/tests/assets/images/testocr.png",
  "type": "image",
  "format": "png",
  "text": "This is a lot of 12 point text to test the\nocr code and see if it works on all types\nof file format.\n\nThe quick brown dog jumped over the\nlazy fox. ...",
  "wordCount": 60,
  "confidence": 95,
  "languages": ["eng", "deu"],
  "width": 640,
  "height": 480,
  "noTextFound": false,
  "bytes": 23359,
  "ms": 1510,
  "charged": { "ocr-page": 1 }
}

PDFs give one item with the whole text (pages marked <!-- page N -->) and a pages array:

{
  "type": "pdf",
  "pageCount": 6,
  "pagesProcessed": 6,
  "confidence": 85,
  "pages": [
    { "page": 1, "method": "ocr", "text": "ELL, I got a good going-over in the morning, ...", "wordCount": 292, "confidence": 93 },
    { "page": 4, "method": "text-layer", "text": "...", "wordCount": 22 }
  ],
  "charged": { "ocr-page": 5, "pdf-text-page": 1 }
}

Failed files return { "url", "error": { "code", "message" } } and are not charged. Codes: unsupported_type (e.g. a link to an HTML page instead of the file), bad_image, bad_pdf, too_large, target_http_404, robots_disallowed, target_timeout. The dataset has two views (Text, Details).

Invoices and receipts in n8n, Make or Zapier

OCR gives you the text; a language model in your workflow turns it into fields. That split keeps the price at $0.002 per page and lets you use your own LLM key and prompt. Real result for a photographed Swiss restaurant receipt (https://upload.wikimedia.org/wikipedia/commons/0/0b/ReceiptSwiss.jpg, languages deu + eng, 1.5 s):

{
  "type": "image",
  "confidence": 80,
  "text": "Berghotel\n\nGrosse Scheidegg\n5818 Grindelwald\n\nFamilie R. Müller\n\nRech. Nr. 4572 30.07. 2007/13 :29: 17\nBar Tisch 7/01\n\n2xLatte Macchiato 4,50\n1xGloki 5.00\n1xSchweinschnitzel 22. 00\n1xChässpätzli 18.50\n\nTotal; = CHE\n\nIncl, 68% Must 54.00 CHF! 32.88\n\nEntspricht in Euro 36.33 EUR\nEs bediente Sie: Ursula\n\nMwSt Nr. : 430 234\n..."
}

The text has typical OCR noise (22. 00, CHE for CHF). An LLM step repairs that easily when you ask for a fixed JSON shape, e.g.

{ "vendor", "invoiceNumber", "date", "currency", "lineItems": [{ "quantity", "description", "amount" }], "total", "vatId" }

n8n template (n8n Cloud works, no Execute Command needed): copy this JSON and paste it into the n8n canvas (Ctrl/Cmd+V). It reads the file URL from the incoming item (invoiceUrl), waits for the OCR result and returns one item with text. Put your Apify API token into the Authorization header (better: an n8n Header Auth credential), then add your LLM node (OpenAI, Anthropic, Gemini, or n8n's Information Extractor) with the prompt above.

{
  "nodes": [
    {
      "name": "OCR invoice (Apify)",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4.2,
      "position": [0, 0],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/4b47~image-pdf-ocr/run-sync-get-dataset-items",
        "sendHeaders": true,
        "headerParameters": { "parameters": [{ "name": "Authorization", "value": "Bearer YOUR_APIFY_TOKEN" }] },
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "={{ JSON.stringify({ urls: [$json.invoiceUrl], languages: ['eng', 'deu'] }) }}",
        "options": { "timeout": 300000 }
      }
    }
  ],
  "connections": {}
}

Make: module Apify → Run an Actor (actor image-pdf-ocr, Run synchronously on, input {"urls": ["{{1.invoiceUrl}}"]}), then Apify → Get Dataset Items with the run's default dataset, then your LLM module. Zapier: the Apify app's run-actor action with the same input, then read the run's dataset items.

PDF invoices from accounting tools usually have a text layer: those pages are read exactly (no OCR errors) for $0.0003 per page.

How much does OCR cost?

No per-file or per-PDF fees, and platform usage is included. Set a maximum cost per run and the actor stops there.

Accuracy: what to expect

Tesseract is excellent on scans, screenshots, printed documents and receipts photographed flat (90-95% confidence on clean scans in our tests). Photos are harder: the actor fixes EXIF rotation, converts to grayscale, stretches contrast and resizes to an OCR-friendly size, and if a photo yields nothing it retries with sparse-text layout and keeps only confident lines. Handwriting, heavy perspective, curved labels and stylized fonts are weak spots of every Tesseract-based tool; check confidence and use minConfidence to drop low-quality results for free.

FAQ

Which languages are supported?

80+: Latin, Cyrillic, Greek, Arabic, Hebrew, Devanagari and other Indic scripts, Thai, Chinese (simplified and traditional), Japanese, Korean and more. Give only the languages present: each extra one slows OCR.

Is my data private?

Files are downloaded by the actor, processed in memory and not stored anywhere except the dataset in your Apify account. No external OCR service is called. The actor respects robots.txt of the file's host.

Why did I get unsupported_type?

The URL returned a web page, not the file. Open the image or PDF itself in the browser and copy that address (it usually ends in .jpg, .png or .pdf). Such errors are free.

Why is a PDF page empty?

It has no text layer and no image big enough to contain text (or OCR found nothing reliable). Empty pages are free.

How fast is it?

About 1-2 seconds per typical image per worker. Workers scale with memory (one per 2 GB, up to 4): give the run 8 GB for 4 parallel workers on large batches.

Can I call it from code or an AI agent?

Yes: the Apify API (run-sync-get-dataset-items returns the text in one request), the JavaScript and Python clients, or the Apify MCP server.

Integrations and API

Export to JSON, CSV, Excel or HTML; connect Google Sheets, Make, Zapier, n8n or webhooks; schedule runs; call the actor through the Apify API or the Apify MCP server.

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