# PDF Text Extractor — PDF to Clean Text JSON (`omao/pdf-text`) Actor Extract clean, structured text from any PDF by URL, page by page. Returns one row per page with de-hyphenated, whitespace-normalized text. Fast, no setup. - **URL**: https://apify.com/omao/pdf-text.md - **Developed by:** [Marouane Oulabass](https://apify.com/omao) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 pdf page extracteds 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.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 ## PDF Text Extractor — PDF to Clean Text JSON **Extract clean, readable text from any PDF by URL — page by page.** Give this actor one or many PDF links and get back structured JSON with one row per page: de-hyphenated, whitespace-normalized text that's ready for search, LLMs, or your database. No OCR setup, no local tooling, no libraries to install. Turn reports, invoices, research papers, manuals, contracts and ebooks into machine-readable text in seconds. ### Who uses this - 🤖 **AI / LLM & RAG pipelines** — chunk PDFs into clean text for embeddings and retrieval. - 📄 **Document processing** — extract text from invoices, contracts, statements and forms. - 🔎 **Search & indexing** — make PDF libraries full-text searchable. - 📚 **Researchers & analysts** — pull text from papers and reports at scale. - 🧑‍💻 **Developers** — skip PDF libraries and edge cases; just get JSON by URL. ### What you get — one clean row per page | Field | Description | |---|---| | `text` | **Clean page text** — de-hyphenated, whitespace-normalized | | `page` | Page number (1-based) | | `pageCount` | Total pages in the document | | `charCount` | Characters extracted on the page | | `pdfUrl` | Source PDF URL | | `ok`, `scrapedAt` | Status & provenance | ### Example **Input** ```json { "pdfUrls": ["https://example.com/annual-report.pdf"], "maxPagesPerPdf": 50 } ```` **Output (one item = one page)** ```json { "pdfUrl": "https://example.com/annual-report.pdf", "page": 1, "pageCount": 48, "text": "Annual Report 2025. In this financial year the company delivered...", "charCount": 2184, "ok": true } ``` ### Why this extractor - ✅ **Clean, usable text** — hyphenated line-breaks rejoined, whitespace normalized. - 📑 **Page-level granularity** — one row per page, so you keep document structure. - 🌐 **Any PDF by URL** — public reports, papers, statements, manuals. - ⚡ **Fast & affordable** — pay only per page extracted. - 🤖 **LLM-ready** — perfect input for chunking, embeddings and RAG. ### Input options - `pdfUrls` *(required)* — one or more PDF URLs (`https://` added if missing). - `maxPagesPerPdf` — cap pages per PDF. Leave empty for all pages. ### FAQ **Does it work on scanned/image PDFs?** It extracts embedded text layers (digital PDFs). Purely scanned image-only PDFs without a text layer won't yield text. **Can I process many PDFs in one run?** Yes — pass a list of URLs; every row carries its `pdfUrl` and `page`. **Does it keep page structure?** Yes — you get one row per page with the page number and total page count. **How is the text cleaned?** Hyphenated words split across line breaks are rejoined and whitespace is normalized for readable output. **Can I export to CSV/Excel/Google Sheets?** Yes — the dataset exports to JSON, CSV, Excel and HTML, or pull it via the API. **What does it cost?** Pay-per-page — you're only charged for pages actually extracted. *** *Tip: feed the per-page `text` straight into your embedding pipeline — each page is already a natural chunk.* # Actor input Schema ## `pdfUrls` (type: `array`): URLs of PDF files to extract text from (https:// added if missing). ## `maxPagesPerPdf` (type: `integer`): Cap the number of pages extracted per PDF. Leave empty for all pages. ## `healthCheckMode` (type: `boolean`): Internal monitoring: extract a canary PDF and FAIL if broken (no billing). ## Actor input object example ```json { "pdfUrls": [ "https://example.com/report.pdf" ], "healthCheckMode": false } ``` # Actor output Schema ## `pages` (type: `string`): All extracted PDF pages (page number, text, char count). # 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://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("omao/pdf-text").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 = { "pdfUrls": ["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("omao/pdf-text").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 '{ "pdfUrls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ] }' | apify call omao/pdf-text --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=omao/pdf-text", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF Text Extractor — PDF to Clean Text JSON", "description": "Extract clean, structured text from any PDF by URL, page by page. Returns one row per page with de-hyphenated, whitespace-normalized text. Fast, no setup.", "version": "0.1", "x-build-id": "dIXCsPg2WIdgevUwg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/omao~pdf-text/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-omao-pdf-text", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/omao~pdf-text/runs": { "post": { "operationId": "runs-sync-omao-pdf-text", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/omao~pdf-text/run-sync": { "post": { "operationId": "run-sync-omao-pdf-text", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "pdfUrls" ], "properties": { "pdfUrls": { "title": "PDF URLs", "type": "array", "description": "URLs of PDF files to extract text from (https:// added if missing).", "items": { "type": "string" } }, "maxPagesPerPdf": { "title": "Max pages per PDF (optional)", "minimum": 1, "type": "integer", "description": "Cap the number of pages extracted per PDF. Leave empty for all pages." }, "healthCheckMode": { "title": "Health-check mode", "type": "boolean", "description": "Internal monitoring: extract a canary PDF and FAIL if broken (no billing).", "default": false } } }, "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 } } } } } } } } } } ```