# PDF Text Extractor – PDF to Text, Metadata & Pages (`haketa/pdf-text-extractor`) Actor Extract clean text and metadata from any PDF by URL: full text, per-page text, page count, title, author, dates and producer. No browser, no OCR needed for text PDFs. Ideal for AI/RAG, search and document data extraction. Export to JSON, CSV or Excel. - **URL**: https://apify.com/haketa/pdf-text-extractor.md - **Developed by:** [Haketa](https://apify.com/haketa) (community) - **Categories:** Developer tools, AI, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 results 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/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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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

PDF Text Extractor πŸ“„

Platform What AI No code Export

Turn any PDF into clean, structured text and metadata.

Give it PDF URLs and get back the **full text**, **per-page text**, and **document metadata** (title, author, page count, dates, producer) β€” clean and ready for **AI/RAG pipelines, search, and data extraction**. Fast, serverless and **no code required**. --- ### ⚑ At a glance | | | |---|---| | πŸ“ **Full text** | Clean plain text from the whole document | | πŸ“‘ **Per-page text** | Optional page-by-page array for chunking | | 🏷️ **Metadata** | Title, author, page count, dates, producer | | πŸ€– **AI/RAG ready** | Feed text straight into LLMs & vector DBs | | πŸ“¦ **Bulk** | Process one PDF or thousands from a list | | ⚑ **Fast** | Pure code β€” no browser, no OCR needed | | πŸ’Ύ **Export** | Excel, CSV, JSON or API | --- ### πŸ“Œ Why this Actor? - πŸ“ **Clean text extraction** β€” the full document as readable plain text, with broken PDF spacing fixed. - πŸ“‘ **Per-page output** β€” an optional `pages` array so you can chunk documents page-by-page for embeddings and RAG. - 🏷️ **Rich metadata** β€” title, author, subject, keywords, creator, producer, creation & modification dates and page count. - πŸ€– **Built for AI** β€” structured JSON output that drops straight into LLM, embedding and search workflows. - πŸ“¦ **Bulk-friendly** β€” pass a big list of PDF URLs and get one clean dataset back. --- ### ✨ What you get (data fields) | Field | Description | |-------|-------------| | `text` | Full extracted text (plain) | | `pages` | Per-page text array *(optional)* | | `pageCount` | Number of pages | | `wordCount` | Extracted word count | | `title` | Document title | | `author` | Document author | | `subject` | Document subject | | `keywords` | Document keywords | | `creator` | Creating application | | `producer` | PDF producer | | `creationDate` / `modificationDate` | Document dates | | `fileName` | File name | | `fileSizeBytes` | File size | | `url` | Source URL | | `error` | Set if extraction failed | --- ### πŸš€ Quick start 1. Click **Try for free / Start**. 2. Add your **PDF URLs** β€” one per line: ```` https://arxiv.org/pdf/1706.03762 https://example.com/report.pdf ```` 3. *(Optional)* Turn on **Include per-page text** for chunking, or set **Max pages**. 4. Click **Save & Start**, then export from the **Dataset** tab. > πŸ’‘ **Tip:** Have a big list? Use **PDF URLs from file / list** to load thousands of URLs from a CSV or Google Sheet. --- ### 🎯 Use cases | Use case | What it delivers | |----------|------------------| | πŸ€– **AI / RAG pipelines** | Clean, chunked text ready for LLMs, embeddings and vector databases. | | πŸ”Ž **Document search** | Index PDF content for full-text search. | | πŸ“Š **Data extraction** | Pull text from reports, invoices, contracts and papers at scale. | | πŸ“š **Research** | Extract text from academic papers and whitepapers. | | πŸ—‚οΈ **Content migration** | Convert PDF libraries into structured text. | | 🧾 **Compliance & archiving** | Turn document sets into searchable text records. | --- ### βš™οΈ Input reference | Input | Type | Default | Description | |-------|------|---------|-------------| | **PDF URLs** | list | sample | PDF URLs to extract. | | **PDF URLs from file / list** | list | β€” | Bulk-load URLs from a file or sheet. | | **Include per-page text** | boolean | `false` | Add a `pages` array. | | **Include document metadata** | boolean | `true` | Extract title, author, dates, etc. | | **Max pages per PDF** | number | `0` | `0` = all pages. | | **Download timeout (seconds)** | number | `60` | Per-PDF download timeout. | | **Proxy / concurrency** | β€” | Datacenter / 5 | Sensible defaults. | #### πŸ“₯ Example input ```json { "pdfUrls": ["https://arxiv.org/pdf/1706.03762"], "splitPages": true, "includeMetadata": true, "maxPages": 0 } ```` *** ### πŸ“€ Example output ```json { "url": "https://arxiv.org/pdf/1706.03762", "fileName": "1706.03762", "fileSizeBytes": 2215244, "pageCount": 15, "wordCount": 8231, "title": "Attention Is All You Need", "author": "", "producer": "pdfTeX-1.40.25", "creationDate": "2024-04-10T21:11:43.000Z", "text": "Provided proper attribution is provided, Google hereby grants permission to reproduce…", "pages": ["…page 1 text…", "…page 2 text…"], "scrapedAt": "2026-07-25T10:00:00.000Z" } ``` *** ### πŸ’‘ Tips & best practices - βœ… **Use `splitPages`** for AI/RAG β€” page-by-page text is easy to chunk and embed. - βœ… **Cap `maxPages`** for very large documents when you only need the first pages. - βœ… **Bulk-load** URLs from a file to process large libraries. - βœ… **Check the `error` field** to spot any URLs that weren't valid PDFs. - βœ… **Scale concurrency** for big batches. *** ### ❓ FAQ **What kinds of PDF work?** Text-based (digitally created) PDFs. Scanned/image-only PDFs have no embedded text layer, so they return no text (OCR is not performed). **Can I get per-page text?** Yes β€” turn on **Include per-page text** to add a `pages` array. **Can I extract metadata?** Yes β€” title, author, subject, keywords, creator, producer and dates are included by default. **Do I need to code?** No. Add PDF URLs, click Start, export. **In what format is the data?** Structured JSON by default, exportable to CSV, Excel, HTML or via API. *** ### πŸ“Š Output & integrations Results are stored in a standard Apify dataset β€” preview as a **table**, export to **JSON, CSV, Excel, HTML**, pull via the **API**, or push to **Google Sheets, Make, Zapier** and more. *** ### βš–οΈ Legal & responsible use This Actor extracts text from PDFs you provide. - Ensure you have the right to download and process each document. - Respect copyright and the terms under which documents are published. - You are responsible for how you use the extracted data. *** ### πŸ›Ÿ Support Found a bug, need an extra field, or want a tweak? **Open an issue** on the Actor's Issues tab.

Happy extracting! πŸ“„

# Actor input Schema ## `pdfUrls` (type: `array`): One PDF URL per line. Each PDF is downloaded and its text + metadata extracted. ## `startUrls` (type: `array`): Optional. Load many PDF URLs from a file, URL, Google Sheet or Request Queue. ## `splitPages` (type: `boolean`): Add a `pages` array with the text of each page separately (useful for chunking into AI/RAG pipelines). The full `text` field is always included. ## `includeMetadata` (type: `boolean`): Extract title, author, subject, keywords, creator, producer and dates from the PDF. ## `maxPages` (type: `integer`): Only extract the first N pages of each PDF. Set 0 for all pages. ## `maxFileSizeMB` (type: `integer`): Skip PDFs larger than this to keep runs fast and cheap. Set 0 for no limit. ## `timeoutSecs` (type: `integer`): How long to wait for each PDF to download before skipping it. Lower values keep runs cheaper by not waiting on slow or dead URLs. ## `proxyConfiguration` (type: `object`): Proxy settings for downloading PDFs. Datacenter proxies (the default) are usually enough. ## `maxConcurrency` (type: `integer`): How many PDFs to process in parallel. Higher finishes big batches faster and cheaper. Recommended: 8–15. ## Actor input object example ```json { "pdfUrls": [ "https://example.com/report.pdf" ], "startUrls": [], "splitPages": false, "includeMetadata": true, "maxPages": 0, "maxFileSizeMB": 50, "timeoutSecs": 20, "proxyConfiguration": { "useApifyProxy": true }, "maxConcurrency": 10 } ``` # Actor output Schema ## `datasetItems` (type: `string`): Download every extracted PDF (JSON, CSV, Excel). ## `runUrl` (type: `string`): Open this run in the Apify Console. ## `fileName` (type: `string`): PDF file name ## `title` (type: `string`): Document title ## `author` (type: `string`): Document author ## `pageCount` (type: `string`): Number of pages ## `wordCount` (type: `string`): Extracted word count ## `text` (type: `string`): Full extracted text ## `subject` (type: `string`): Document subject ## `keywords` (type: `string`): Document keywords ## `creator` (type: `string`): Creating application ## `producer` (type: `string`): PDF producer ## `creationDate` (type: `string`): Creation date ## `modificationDate` (type: `string`): Modification date ## `fileSizeBytes` (type: `string`): File size in bytes ## `error` (type: `string`): Error, if extraction failed ## `url` (type: `string`): PDF URL ## `scrapedAt` (type: `string`): ISO 8601 timestamp # 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 = { "pdfUrls": [ "https://arxiv.org/pdf/1706.03762" ], "startUrls": [], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("haketa/pdf-text-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "pdfUrls": ["https://arxiv.org/pdf/1706.03762"], "startUrls": [], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("haketa/pdf-text-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "pdfUrls": [ "https://arxiv.org/pdf/1706.03762" ], "startUrls": [], "proxyConfiguration": { "useApifyProxy": true } }' | apify call haketa/pdf-text-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=haketa/pdf-text-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF Text Extractor – PDF to Text, Metadata & Pages", "description": "Extract clean text and metadata from any PDF by URL: full text, per-page text, page count, title, author, dates and producer. No browser, no OCR needed for text PDFs. Ideal for AI/RAG, search and document data extraction. Export to JSON, CSV or Excel.", "version": "1.0", "x-build-id": "PSnJBcmkkYQhVkf0b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/haketa~pdf-text-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-haketa-pdf-text-extractor", "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/haketa~pdf-text-extractor/runs": { "post": { "operationId": "runs-sync-haketa-pdf-text-extractor", "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/haketa~pdf-text-extractor/run-sync": { "post": { "operationId": "run-sync-haketa-pdf-text-extractor", "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": { "pdfUrls": { "title": "PDF URLs", "type": "array", "description": "One PDF URL per line. Each PDF is downloaded and its text + metadata extracted.", "items": { "type": "string" } }, "startUrls": { "title": "PDF URLs from file / list (advanced)", "type": "array", "description": "Optional. Load many PDF URLs from a file, URL, Google Sheet or Request Queue.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "splitPages": { "title": "Include per-page text", "type": "boolean", "description": "Add a `pages` array with the text of each page separately (useful for chunking into AI/RAG pipelines). The full `text` field is always included.", "default": false }, "includeMetadata": { "title": "Include document metadata", "type": "boolean", "description": "Extract title, author, subject, keywords, creator, producer and dates from the PDF.", "default": true }, "maxPages": { "title": "Max pages per PDF", "minimum": 0, "type": "integer", "description": "Only extract the first N pages of each PDF. Set 0 for all pages.", "default": 0 }, "maxFileSizeMB": { "title": "Max file size (MB)", "minimum": 0, "type": "integer", "description": "Skip PDFs larger than this to keep runs fast and cheap. Set 0 for no limit.", "default": 50 }, "timeoutSecs": { "title": "Download timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "How long to wait for each PDF to download before skipping it. Lower values keep runs cheaper by not waiting on slow or dead URLs.", "default": 20 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings for downloading PDFs. Datacenter proxies (the default) are usually enough.", "default": { "useApifyProxy": true } }, "maxConcurrency": { "title": "Max concurrent requests", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many PDFs to process in parallel. Higher finishes big batches faster and cheaper. Recommended: 8–15.", "default": 10 } } }, "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 } } } } } } } } } } ```