# Any URL OCR (`caleno/any-url-ocr`) Actor Extract text from any image or PDF by URL using Tesseract OCR. Multi-language, PDF page-by-page, no paid OCR API. - **URL**: https://apify.com/caleno/any-url-ocr.md - **Developed by:** [Caleno](https://apify.com/caleno) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 81.8% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 ocr per pages 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 ## URL OCR 🔤 **Extract text from any image or PDF by URL — automatic language detection, PDF support, no paid OCR API.** Point it at an image or PDF URL and get the text back. It uses a modern deep-learning OCR engine, so it reads the hard cases too: screenshots, UI and menus, photos, low-contrast scans and light-text-on-dark backgrounds — not just clean black-on-white documents. --- ### ✨ What it does - 🖼️ **Images & 📄 PDFs** — PNG, JPG, WebP, TIFF, and multi-page PDFs (processed page by page). - 🌍 **Automatic language detection** — no need to pick a language. English, Hungarian, German, French, Spanish, Italian, Portuguese, Dutch, Polish and more. - 🌓 **Dark-background aware** — automatically inverts light-text-on-dark images (navbars, hero sections, dark UI) that plain OCR reads as garbage. - 🎯 **Clean output** — plain text plus word/character counts and the detected language; PDFs also return per-page text. - 💸 **No external OCR API** — everything runs inside the actor, keeping it cheap. --- ### 🎯 Use cases - Digitize scanned documents, invoices and receipts - Pull text out of screenshots, app UIs and social posts - Make PDFs searchable (page-by-page text) - Feed extracted text into search, AI, translation or data pipelines - Read text from product photos and signage --- ### 📥 Input | Field | Description | |-------|-------------| | **Image / PDF URLs** | One or more image or PDF URLs to read. | | **PDF render quality (DPI)** | Resolution for rasterizing PDF pages (default 200). | | **Proxy** | Optional proxy for downloading the files. | #### Example input ```json { "urls": [ "https://example.com/invoice.pdf", "https://example.com/screenshot.png" ] } ```` *** ### 📤 Output One dataset item per URL: ```json { "url": "https://example.com/screenshot.png", "type": "image", "detectedLanguage": "en", "words": 128, "characters": 742, "text": "The full extracted text …", "width": 1440, "height": 900 } ``` For a PDF you also get `pageCount` and a `pages` array with the text of each page. *** ### ⚙️ How it works The file is downloaded, images are preprocessed (upscaled and cleaned up so small or low-contrast text is readable), and the text is recognized with a deep-learning OCR model. The language is detected from the result automatically. PDFs are rasterized page by page and each page is read separately. *** ### 💳 Pricing **Pay per page:** you are charged per page processed — an image counts as one page, a PDF is charged per page. Failed URLs are free. *** ### ❓ Notes - Best results come from real text. Stylized logos and heavy artwork are graphics, not text, so they may not read cleanly. - Very low-resolution or blurry sources reduce accuracy — a higher-quality image gives better text. Questions or a feature request? Open an issue on the actor page — the actor is actively maintained. 🔄 # Actor input Schema ## `urls` (type: `array`): URLs of images (PNG, JPG, WebP, TIFF, …) or PDFs. Each one is downloaded and its text is extracted with OCR. The language is detected automatically. PDFs are processed page by page. ## `pdfDpi` (type: `integer`): Resolution used to rasterize PDF pages before OCR. Higher = sharper text but slower. 200 is a good default. ## `proxyConfiguration` (type: `object`): Optional proxy for downloading the files (e.g. for geo-specific sources). ## Actor input object example ```json { "urls": [ "https://tesseract.projectnaptha.com/img/eng_bw.png" ], "pdfDpi": 200, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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://tesseract.projectnaptha.com/img/eng_bw.png" ] }; // Run the Actor and wait for it to finish const run = await client.actor("caleno/any-url-ocr").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://tesseract.projectnaptha.com/img/eng_bw.png"] } # Run the Actor and wait for it to finish run = client.actor("caleno/any-url-ocr").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://tesseract.projectnaptha.com/img/eng_bw.png" ] }' | apify call caleno/any-url-ocr --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=caleno/any-url-ocr", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Any URL OCR", "description": "Extract text from any image or PDF by URL using Tesseract OCR. Multi-language, PDF page-by-page, no paid OCR API.", "version": "0.1", "x-build-id": "ecYxuI6kIGCjOMkWG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/caleno~any-url-ocr/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-caleno-any-url-ocr", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/caleno~any-url-ocr/runs": { "post": { "operationId": "runs-sync-caleno-any-url-ocr", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/caleno~any-url-ocr/run-sync": { "post": { "operationId": "run-sync-caleno-any-url-ocr", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "urls": { "title": "🔗 Image / PDF URLs", "type": "array", "description": "URLs of images (PNG, JPG, WebP, TIFF, …) or PDFs. Each one is downloaded and its text is extracted with OCR. The language is detected automatically. PDFs are processed page by page.", "items": { "type": "string" } }, "pdfDpi": { "title": "🖨️ PDF render quality (DPI)", "minimum": 72, "maximum": 400, "type": "integer", "description": "Resolution used to rasterize PDF pages before OCR. Higher = sharper text but slower. 200 is a good default.", "default": 200 }, "proxyConfiguration": { "title": "🌐 Proxy", "type": "object", "description": "Optional proxy for downloading the files (e.g. for geo-specific sources).", "default": { "useApifyProxy": 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 } } } } } } } } } } ```