# Pdf Scraper (`webscrap18/pdf-scraper`) Actor A high-performance Apify Actor that inspects, classifies, and extracts structured data from PDF files. It intelligently detect whether a PDF is text-based or scanned and converts it into clean, formatted Markdown. - **URL**: https://apify.com/webscrap18/pdf-scraper.md - **Developed by:** [WebScrap](https://apify.com/webscrap18) (community) - **Categories:** Automation, Developer tools, Lead generation - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $14.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 Inspector Actor A high-performance **Apify Actor** that inspects, classifies, and extracts structured data from PDF files. It intelligently detect whether a PDF is text-based or scanned and converts it into clean, formatted Markdown. ### 🚀 Features - **⚡️ Blazing Fast**: Native Rust implementation ensures minimal latency and low memory usage. - **🧠 Smart Detection**: Automatically classifies PDFs as `TextBased`, `Scanned`, `ImageBased`, or `Mixed`. - **📝 Clean Markdown**: Extracts text and formatting (headers, lists, code blocks, bold/italic) into LLM-ready Markdown. - **⚙️ Highly Configurable**: Fine-tune detection sensitivity, font sizes, and formatting rules. - **🔒 Privacy First**: All processing happens securely within the Actor container. --- ### 📥 Input The Actor accepts a simple JSON input. You can configure the URL and various processing options. #### Example Input ```json { "url": "https://pdfobject.com/pdf/sample.pdf", "detect_headers": true, "detect_lists": true, "fix_hyphenation": true } ```` #### Configuration Options | Field | Type | Default | Description | | :--- | :--- | :--- | :--- | | **`url`** | `String` | **Required** | Direct URL to the PDF file. | | `detect_headers` | `Boolean` | `true` | Detect headers based on font size hierarchy. | | `detect_lists` | `Boolean` | `true` | Detect bullet points and numbered lists. | | `detect_code` | `Boolean` | `true` | Detect code blocks using monospace fonts. | | `fix_hyphenation` | `Boolean` | `true` | Attempt to rejoin words broken across lines. | | `base_font_size` | `Number` | `Auto` | Override base font size (useful if headers aren't detected). | | `remove_page_numbers` | `Boolean` | `true` | Cleanup standalone page numbers. | | `format_urls` | `Boolean` | `true` | Convert URLs into Markdown links. | *** ### 📤 Output The Actor saves the result to the **Default Key-Value Store** and **Dataset**. #### Example Output JSON ```json { "url": "https://pdfobject.com/pdf/sample.pdf", "inspection_result": { "pdf_type": "TextBased", "text": null, "markdown": "# Sample PDF\n\nThis is a header...\n\n- List item 1\n- List item 2", "page_count": 1, "processing_time_ms": 12 } } ``` #### Output Fields - **`pdf_type`**: one of `TextBased`, `Scanned`, `ImageBased`, `Mixed`. - **`markdown`**: The extracted content formatted as Markdown. - **`page_count`**: Total number of pages in the document. - **`processing_time_ms`**: Time taken to process the file in milliseconds. *** # Actor input Schema ## `url` (type: `string`): Direct URL to the PDF file to inspect. ## `detect_headers` (type: `boolean`): Detect headers based on font size. ## `detect_lists` (type: `boolean`): Detect bullet lists and numbered lists. ## `base_font_size` (type: `number`): Optional override for base font size. Affects header and table detection. Leave empty for auto-detection. ## `fix_hyphenation` (type: `boolean`): Attempt to rejoin words broken across lines. ## `detect_bold` (type: `boolean`): Detect bold text by font weight or name. ## `detect_italic` (type: `boolean`): Detect italic text by font style or name. ## `include_images` (type: `boolean`): Include image placeholders in output. ## `include_links` (type: `boolean`): Include extracted hyperlinks. ## Actor input object example ```json { "url": "https://pdfobject.com/pdf/sample.pdf", "detect_headers": true, "detect_lists": true, "fix_hyphenation": true, "detect_bold": true, "detect_italic": true, "include_images": true, "include_links": true } ``` # 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 = { "url": "https://pdfobject.com/pdf/sample.pdf" }; // Run the Actor and wait for it to finish const run = await client.actor("webscrap18/pdf-scraper").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 = { "url": "https://pdfobject.com/pdf/sample.pdf" } # Run the Actor and wait for it to finish run = client.actor("webscrap18/pdf-scraper").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 '{ "url": "https://pdfobject.com/pdf/sample.pdf" }' | apify call webscrap18/pdf-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=webscrap18/pdf-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pdf Scraper", "description": "A high-performance Apify Actor that inspects, classifies, and extracts structured data from PDF files. It intelligently detect whether a PDF is text-based or scanned and converts it into clean, formatted Markdown.", "version": "0.1", "x-build-id": "Him2fO0CoZ7dHFCfr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/webscrap18~pdf-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-webscrap18-pdf-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/webscrap18~pdf-scraper/runs": { "post": { "operationId": "runs-sync-webscrap18-pdf-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/webscrap18~pdf-scraper/run-sync": { "post": { "operationId": "run-sync-webscrap18-pdf-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "PDF URL", "type": "string", "description": "Direct URL to the PDF file to inspect.", "default": "https://ontheline.trincoll.edu/images/bookdown/sample-local-pdf.pdf" }, "detect_headers": { "title": "Detect Headers", "type": "boolean", "description": "Detect headers based on font size.", "default": true }, "detect_lists": { "title": "Detect Lists", "type": "boolean", "description": "Detect bullet lists and numbered lists.", "default": true }, "base_font_size": { "title": "Base Font Size", "type": "number", "description": "Optional override for base font size. Affects header and table detection. Leave empty for auto-detection." }, "fix_hyphenation": { "title": "Fix Hyphenation", "type": "boolean", "description": "Attempt to rejoin words broken across lines.", "default": true }, "detect_bold": { "title": "Detect Bold", "type": "boolean", "description": "Detect bold text by font weight or name.", "default": true }, "detect_italic": { "title": "Detect Italic", "type": "boolean", "description": "Detect italic text by font style or name.", "default": true }, "include_images": { "title": "Include Images", "type": "boolean", "description": "Include image placeholders in output.", "default": true }, "include_links": { "title": "Include Links", "type": "boolean", "description": "Include extracted hyperlinks.", "default": true } } }, "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 } } } } } } } } } } ```