# PDF to Markdown Converter with OCR (`larchrow/document-to-markdown-ocr`) Actor

Convert PDFs, scanned documents, DOCX and XLSX to clean Markdown, text and tables. Built-in OCR for scans. Ideal for RAG pipelines and LLM ingestion.

- **URL**: https://apify.com/larchrow/document-to-markdown-ocr.md
- **Developed by:** [Larchrow](https://apify.com/larchrow) (community)
- **Categories:** Developer tools, AI
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 pages

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## PDF to Markdown Converter with OCR

> 🇬🇧 English | 🇩🇪 Deutsch below

**Convert PDFs, scanned documents, images, Word and Excel files into clean Markdown, plain text and structured tables, for RAG, AI agents and data extraction.**

- Text PDFs: headings, de-hyphenation, tables as Markdown and JSON rows
- Scans and images: low-cost local OCR (Tesseract); in `auto` mode only text-less pages are OCR'd and billed as OCR
- DOCX headings, lists and tables; every XLSX/CSV sheet as a table
- Pay per page; failed files are free; no external AI; data never leaves the run

SEO: PDF to Markdown, PDF to text, OCR, scanned PDF OCR, PDF table extraction, DOCX to Markdown, Excel to JSON, document parser, RAG ingestion, Tesseract OCR API, PDF in Text umwandeln, Texterkennung

***

### 🇩🇪 Deutsch

**Wandelt PDFs, gescannte Dokumente, Bilder, Word- und Excel-Dateien in sauberes Markdown, Plain-Text und strukturierte Tabellen um. Ideal für RAG, KI-Agenten, Datenextraktion und Archivierung.**

- 📄 **PDF mit Textlayer:** Markdown mit Überschriften (erkannt an der Schriftgröße), Silbentrennung entfernt, Tabellen als Markdown **und** als JSON-Zeilen
- 🔍 **Gescannte PDFs und Bilder:** **günstige lokale OCR** (Tesseract, Deutsch, Englisch, Französisch). Im Modus `auto` wird nur bei Seiten ohne Text OCR genutzt, und nur diese Seiten kosten den OCR-Preis.
- 📝 **DOCX:** Überschriften, Listen und Tabellen · 📊 **XLSX/CSV:** jedes Blatt als Tabelle (Trennzeichen automatisch erkannt)
- 💶 **Fair:** Abrechnung pro Seite; fehlgeschlagene Dateien kosten nichts. Es gibt keine externe KI und keine Weitergabe an Dritte; die Verarbeitung läuft komplett im Actor.

**Ausgabe pro Datei:** url, type, pages, totalPages, ocrPages, ocrEngine, markdown, text, tables\[{page, rows}], metadata, chars, ok/error

**Für wen:** RAG-Pipelines und Chatbots, Rechnungs- und Vertragsauswertung, Ausschreibungsunterlagen, Forschung, Digitalisierung von Scans, n8n/Make/Zapier-Workflows.

# Actor input Schema

## `urls` (type: `array`):

Datei-URLs (PDF, DOCX, XLSX, CSV, PNG/JPG/TIFF).

## `ocr` (type: `string`):

OCR.

## `ocrLanguage` (type: `string`):

OCR-Sprache (de, en, fra …).

## `ocrDpi` (type: `integer`):

OCR-DPI.

## `extractTables` (type: `boolean`):

Tabellen erkennen.

## `tableStrategy` (type: `string`):

Tabellen-Strategie.

## `includeText` (type: `boolean`):

Zusätzlich Plain-Text ausgeben.

## `maxPages` (type: `integer`):

Max. Seiten pro Dokument.

## Actor input object example

```json
{
  "urls": [
    "https://arxiv.org/pdf/1706.03762"
  ],
  "ocr": "auto",
  "ocrLanguage": "de",
  "ocrDpi": 200,
  "extractTables": true,
  "tableStrategy": "lines",
  "includeText": true,
  "maxPages": 200
}
```

# Actor output Schema

## `results` (type: `string`):

Alle Ergebnisse im Standard-Dataset / all results in the default dataset

## `files` (type: `string`):

Erzeugte Dateien im Key-Value-Store / generated files

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "urls": [
        "https://arxiv.org/pdf/1706.03762"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("larchrow/document-to-markdown-ocr").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "urls": ["https://arxiv.org/pdf/1706.03762"] }

# Run the Actor and wait for it to finish
run = client.actor("larchrow/document-to-markdown-ocr").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "urls": [
    "https://arxiv.org/pdf/1706.03762"
  ]
}' |
apify call larchrow/document-to-markdown-ocr --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,larchrow/document-to-markdown-ocr"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/STMiokpAZq3MEr0cS/builds/sZod9192VdX8N7P5e/openapi.json