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PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR

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Converts PDF to Markdown and Word to Markdown – also PowerPoint and Excel – for AI and RAG: tables kept as Markdown tables and JSON rows, scanned PDFs read with OCR in 8 languages, one row per document, page or chunk. Fills in the files Website Content Crawler downloads but leaves empty.

Pricing

from $0.35 / 1,000 pages

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15 days ago

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You can access the PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

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          "urls": {
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            "type": "array",
            "description": "Links to PDF, Word (.docx), Excel (.xlsx, .xls), PowerPoint (.pptx), HTML, CSV, JSON, XML or text files — one per line, e.g. `https://arxiv.org/pdf/1706.03762`.",
            "items": {
              "type": "string"
            }
          },
          "crawler_dataset": {
            "title": "Website Content Crawler run or dataset ID",
            "type": "string",
            "description": "Convert the files a **Website Content Crawler** run downloaded (option 'Save files'). The crawler stores them with an empty `text` and `markdown`; this fills them in, with the same `url`, `crawl` and `metadata` fields, so the results merge straight into your crawl. Paste the run ID or its dataset ID."
          },
          "output": {
            "title": "Output",
            "enum": [
              "documents",
              "pages",
              "chunks"
            ],
            "type": "string",
            "description": "`documents` (default) = one row per file with the full Markdown — a row can hold tens of thousands of characters, so fetch a few rows at a time. `pages` = one row per page — handy for citations. `chunks` = ready-made pieces for vector databases and RAG, with the pages each chunk covers.",
            "default": "documents"
          },
          "ocr": {
            "title": "Text recognition (scanned pages)",
            "enum": [
              "auto",
              "off",
              "force"
            ],
            "type": "string",
            "description": "`auto` (default): pages without a text layer (scans, photos of paper) are read with OCR and charged as OCR pages; all others are read directly. `off`: never OCR. `force`: OCR every page, for PDFs whose text layer is broken. Pages scanned sideways or upside down are turned automatically. A page whose text recognition fails or stays unreliable is returned with a warning and not charged.",
            "default": "auto"
          },
          "ocr_languages": {
            "title": "OCR languages",
            "type": "array",
            "description": "Tesseract language codes of the scanned documents, e.g. `eng`, `deu`, `fra`, `spa`, `ita`, `nld`, `pol`, `por`. Fewer languages recognise faster and more accurately.",
            "items": {
              "type": "string",
              "enum": [
                "eng",
                "deu",
                "fra",
                "spa",
                "ita",
                "nld",
                "pol",
                "por"
              ],
              "enumTitles": [
                "English",
                "German",
                "French",
                "Spanish",
                "Italian",
                "Dutch",
                "Polish",
                "Portuguese"
              ]
            },
            "default": [
              "eng",
              "deu"
            ]
          },
          "extract_tables": {
            "title": "Extract tables",
            "type": "boolean",
            "description": "Detects tables in PDFs — ruled tables and blocks of figures without lines — and returns them row by row as Markdown tables in place and as structured rows in `tables`. Excel and Word tables are always included.",
            "default": true
          },
          "chunk_size": {
            "title": "Chunk size (characters)",
            "minimum": 200,
            "maximum": 20000,
            "type": "integer",
            "description": "Only for output `chunks` (200–20,000 characters). Chunks break at paragraph boundaries; a table stays in one piece where it fits, a longer one is cut between rows and repeats its header row.",
            "default": 2000
          },
          "chunk_overlap": {
            "title": "Chunk overlap (characters)",
            "minimum": 0,
            "maximum": 5000,
            "type": "integer",
            "description": "Only for output `chunks` (0–5,000 characters). Whole paragraphs from the end of one chunk are repeated at the start of the next, up to this length.",
            "default": 200
          },
          "max_pages": {
            "title": "Maximum pages per document",
            "minimum": 1,
            "type": "integer",
            "description": "Longer PDFs are cut off here and say so in `warnings`. Protects your budget from a surprise 3,000-page file. A document that runs out of time returns — and is charged for — only the pages it finished.",
            "default": 500
          },
          "max_file_mb": {
            "title": "Maximum file size (MB)",
            "minimum": 1,
            "maximum": 500,
            "type": "integer",
            "description": "1–500 MB. Larger files are skipped with a message, and not charged.",
            "default": 50
          }
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              "userId": {
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              "startedAt": {
                "type": "string",
                "format": "date-time",
                "example": "2025-01-08T00:00:00.000Z"
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              "finishedAt": {
                "type": "string",
                "format": "date-time",
                "example": "2025-01-08T00:00:00.000Z"
              },
              "status": {
                "type": "string",
                "example": "READY"
              },
              "meta": {
                "type": "object",
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                  "origin": {
                    "type": "string",
                    "example": "API"
                  },
                  "userAgent": {
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              },
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                  },
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                "example": 0.00005
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PDF to Markdown & Word to Markdown – Excel, PowerPoint, OCR OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR API in Python

PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR API in JavaScript

PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR API through CLI

PDF & DOCX to Markdown Converter – Excel, PowerPoint, OCR API

PDF to Markdown & JSON Converter (Docling)

actorzlab/docling-pdf-converter

Convert PDF documents to clean Markdown, structured JSON, and plain text using IBM's open-source Docling AI. Handles text PDFs and scanned documents (OCR), extracts tables and images. No external API key required — runs fully on-device.

Khalil Drissi

PDF to Markdown Converter - AI-Powered with OCR & Tables

clearpath/pdf-to-markdown-api

Convert PDFs to clean Markdown with GPU-accelerated AI. Extracts tables, LaTeX formulas, and images from complex layouts. Supports OCR for scanned docs in 8 languages. Batch process hundreds of PDFs in parallel via URL, upload, or API.

ClearPath

Markdown Converter

jindrich.bar/markdown-converter

A simple Actor for converting pdf / doc / docx files to Markdown.

Jindřich Bär

PDF to Markdown Converter

web.harvester/pdf-to-markdown-converter

Convert PDFs to clean Markdown with optional OCR for scanned documents. Uses PDF.js for text extraction and Tesseract.js for optical character recognition.

Web Harvester

Convert To Markdown

datavault/convert-to-markdown

Convert to Markdown, converts documents, spreadsheets, images (OCR), audio (transcription), and web/data files into clean Markdown. It runs fully locally, requires no API keys, and is ideal for LLMs, docs, and archiving.

Datavault

PDF to Text Extractor - PDF Parser & PDF to Markdown

gochujang/pdf-text-extractor

Extract text from PDF URLs or base64 files with this PDF extractor and PDF parser. Get per-page text, PDF to Markdown, or RAG chunks; optionally extract tables and OCR scanned pages.

Hojun Lee

281

DOCX to Markdown — Word, PowerPoint & Excel for AI Agents

lizaraco/office-docs-to-markdown

Convert DOCX, PPTX, and XLSX files to clean, LLM-ready markdown at scale. Headings, tables, slides, and sheets preserved. Never-fail runs, per-document output. The Office twin of PDF-to-Markdown.

Shawn Downs

PDF & DOCX to Markdown — Document Extractor for LLM/RAG

fetchbase/document-to-markdown

Convert PDF and Word (DOCX) documents into clean Markdown, text, or JSON. Smart PDF paragraph reflow, page markers for RAG citations, full DOCX structure (headings, lists, tables), custom auth headers. No browser — parses in seconds. Charged per page processed — no startup fee.

Fetchbase

PDF to Markdown — Tables + OCR, for RAG & AI Agents

lizaraco/pdf-to-markdown

Convert PDFs to clean markdown at scale: layout-aware text extraction, table handling, and a vision-model OCR tier for scanned or broken pages. Per-page transparency, never-fail runs.

Shawn Downs

PDF Text Extractor & Markdown for LLM RAG: OCR, Tables, Chunks

pankovs/pdf-to-llm-markdown

Convert PDF to clean Markdown for LLM & RAG pipelines: text extractor with OCR fallback (50 languages), GitHub-flavored table reconstruction (incl. tables spanning pages), right-to-left script support, and optional RAG chunking. ~$0.008 for a 20-page text PDF. Pay only for pages with real text.