# Image to Markdown (`abotapi/any-doc-parser`) Actor Image to Markdown converts images and scanned PDFs into structured Markdown using AI-powered document understanding. It recognizes text, tables, mathematical formulas (LaTeX), and figures while preserving the correct reading order and document layout. - **URL**: https://apify.com/abotapi/any-doc-parser.md - **Developed by:** [AbotAPI](https://apify.com/abotapi) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 5 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### What does Image to Markdown do? Image to Markdown converts images and scanned PDFs into structured **Markdown** using AI-powered document understanding. It recognizes **text**, **tables**, **mathematical formulas (LaTeX)**, and **figures** while preserving the correct reading order and document layout. #### Key features - **Layout analysis** — Understands document structure (titles, paragraphs, figures, captions) - **Table extraction** — Recognizes table structures and outputs them as Markdown tables - **Formula recognition** — Detects mathematical formulas and converts them to LaTeX - **PDF support** — Process multi-page PDF documents - **Multiple input methods** — Upload files directly or provide URLs ### Use cases - **Academic paper digitization** — Convert scanned research papers with equations into editable Markdown/LaTeX - **Technical document processing** — Parse engineering specs, datasheets, and manuals preserving tables and formulas - **Invoice and receipt parsing** — Extract structured data from scanned financial documents - **Book digitization** — Convert scanned book pages into searchable, editable text - **Data pipeline integration** — Use the Apify API to automate document parsing in your workflows - **RAG / LLM preparation** — Convert documents to Markdown for use as context in AI applications ### How to use 1. **Upload files** or **provide URLs** to images (PNG, JPEG) or PDF documents 2. Run the Actor and get structured Markdown output ### Supported file formats | Format | Extensions | |--------|-----------| | **PDF** | `.pdf` | | **PNG** | `.png` | | **JPEG** | `.jpg`, `.jpeg` | ### Input The Actor accepts **uploaded files** or **URLs** pointing to images or PDFs. All other parameters are optional with sensible defaults. - **Upload Files** — Drag and drop image or PDF files directly - **URLs** — List of URLs pointing to documents to parse - **Include Metadata** — Include processing metadata in output (default: `true`) - **Output Format** — Choose between Dataset (JSON), Key-Value Store (MD files), or both (default: `both`) ### Output The Actor outputs results to the **Dataset** (JSON) and/or **Key-Value Store** (Markdown files), depending on the `outputFormat` setting. #### Dataset output example ```json { "source": "https://example.com/document.png", "success": true, "markdown": "## Introduction\n\nThe equation $E = mc^2$ describes...\n\n| Column A | Column B |\n|----------|----------|\n| Value 1 | Value 2 |", "format": "png", "sizeBytes": 245000, "processingTimeMs": 3500, "pagesProcessed": 1, "metadata": { "parsed_at": "2026-03-07T10:00:00.000000", "processing_time_ms": 3500, "file_size_bytes": 245000 } } ```` #### Key-Value Store output When using `keyValueStore` or `both` output format, each successfully parsed document is saved as a `.md` file in the Key-Value Store, ready to download or use in downstream workflows. # Actor input Schema ## `files` (type: `array`): Upload image or PDF files to parse (PNG, JPEG, PDF) ## `urls` (type: `array`): URLs of images or PDF documents to parse ## `includeMetadata` (type: `boolean`): Include file metadata (filename, format, size, processing time) in output ## `outputFormat` (type: `string`): How to output the parsed content ## Actor input object example ```json { "files": [], "urls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ], "includeMetadata": true, "outputFormat": "both" } ``` # Actor output Schema ## `results` (type: `string`): Structured Markdown results with metadata for each parsed document ## `markdownFiles` (type: `string`): Individual .md files for each parsed document # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ] }; // Run the Actor and wait for it to finish const run = await client.actor("abotapi/any-doc-parser").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"] } # Run the Actor and wait for it to finish run = client.actor("abotapi/any-doc-parser").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).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://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ] }' | apify call abotapi/any-doc-parser --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=abotapi/any-doc-parser", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Image to Markdown", "description": "Image to Markdown converts images and scanned PDFs into structured Markdown using AI-powered document understanding. It recognizes text, tables, mathematical formulas (LaTeX), and figures while preserving the correct reading order and document layout.", "version": "2.0", "x-build-id": "lhbKIpOefhEdiXXd9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/abotapi~any-doc-parser/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-abotapi-any-doc-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/abotapi~any-doc-parser/runs": { "post": { "operationId": "runs-sync-abotapi-any-doc-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/abotapi~any-doc-parser/run-sync": { "post": { "operationId": "run-sync-abotapi-any-doc-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "files": { "title": "Upload Files", "type": "array", "description": "Upload image or PDF files to parse (PNG, JPEG, PDF)", "default": [] }, "urls": { "title": "URLs", "type": "array", "description": "URLs of images or PDF documents to parse", "items": { "type": "string" }, "default": [] }, "includeMetadata": { "title": "Include Metadata", "type": "boolean", "description": "Include file metadata (filename, format, size, processing time) in output", "default": true }, "outputFormat": { "title": "Output Format", "enum": [ "dataset", "keyValueStore", "both" ], "type": "string", "description": "How to output the parsed content", "default": "both" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```