# AI License Plate Recognizer (OCR) (`acme-ai/plate-recognizer-ai`) Actor Read vehicle license plates from image URLs using AI vision. Returns plate, confidence, vehicle make/model/color, day/night and image metadata as JSON. Batch or real-time API. - **URL**: https://apify.com/acme-ai/plate-recognizer-ai.md - **Developed by:** [Acme AI](https://apify.com/acme-ai) (community) - **Categories:** AI, Other, Travel - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $350.00 / 1,000 license plate recognizeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## 🚗 AI License Plate Recognizer (OCR) Read vehicle **license plates from image URLs** using AI vision. Send one or more public image links (JPG, PNG, WebP and more) and get a clean JSON record per image: the **plate number**, a **confidence** score, the **vehicle** make/model/color, whether it's **day or night**, image **metadata**, and a **reason** when a plate can't be read (dirty plate, glare, rain, blur...). > 🎯 **Built for plates.** Instead of a generic OCR that dumps raw text, this Actor is prompted specifically to find and normalize the license plate and describe the vehicle and scene around it. --- ### What you get | | Field | Description | |---|---|---| | 🔑 | `licensePlate` | The recognized plate, uppercased and normalized (main field) | | 🎯 | `plateConfidence` | Confidence of the read (0-1) | | 🌍 | `plateRegion` | Plate country/standard when identifiable (e.g. "Brazil (Mercosul)") | | 🌗 | `timeOfDay` | `day`, `night` or `unknown` | | 🌦️ | `weather` | Scene weather: `clear`, `rain`, `fog`, `snow`, `cloudy`, `unknown` | | 🔍 | `imageQuality` | Perceived quality: `good`, `fair`, `poor` | | 📐 | `viewAngle` | Camera view: `front`, `rear`, `side`, `unknown` | | 🚙 | `vehicleMake` / `Model` / `Color` / `Type` | Vehicle attributes (type: car, motorcycle, truck...) | | 🖼️ | `imageWidth` / `imageHeight` / `imageFormat` / `imageSizeBytes` | Image pixel size, format and byte size (flat, one column each) | | ⚠️ | `failureReason` | Why the plate couldn't be read, when applicable | --- ### How to use **1. Provide image URLs** ```json { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg", "https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg" ] } ```` Supports **JPG/JPEG**, **PNG**, **WebP** and other common image formats. Up to **50 images per run** (send larger volumes via sequential calls). **2. Run it** - each image is downloaded and analyzed in parallel. **3. Read the JSON** - one record per image in the dataset (export as JSON, CSV or Excel), or get it synchronously via the Standby API. *** ### Pricing Charged **per image where a plate is read** (event `plate-recognized`). - Images that fail to download: **not charged** - Images where no plate is readable: **not charged** *** ### Example output ```json [ { "imageUrl": "https://.../car.jpg", "success": true, "licensePlate": "ABC1D23", "plateConfidence": 0.97, "plateRegion": "Brazil (Mercosul)", "timeOfDay": "day", "weather": "clear", "imageQuality": "good", "viewAngle": "front", "vehicleMake": "Volkswagen", "vehicleModel": "Gol", "vehicleColor": "silver", "vehicleType": "car", "imageWidth": 1280, "imageHeight": 720, "imageFormat": "jpg", "imageSizeBytes": 184320, "failureReason": null, "processedAt": "2026-01-01T12:00:00.000Z", "error": null }, { "imageUrl": "https://.../blurry.jpg", "success": false, "licensePlate": null, "plateConfidence": null, "plateRegion": null, "timeOfDay": "night", "weather": "rain", "imageQuality": "poor", "viewAngle": "rear", "vehicleMake": null, "vehicleModel": null, "vehicleColor": "black", "vehicleType": "car", "imageWidth": 800, "imageHeight": 600, "imageFormat": "jpg", "imageSizeBytes": 95000, "failureReason": "glare", "processedAt": "2026-01-01T12:00:01.000Z", "error": null } ] ``` *** ### FAQ **Which images work best?** Clear photos where the plate is visible. Heavy glare, motion blur, dirt, rain or very low resolution may prevent a read - the reason is reported in `failureReason`. **What plate formats are supported?** The model reads plates from most regions and reports the standard in `plateRegion` when it can identify it. **Can I call it in real time?** Yes. The Standby endpoint `POST /recognize` responds synchronously. See below. **Do failed images cost anything?** No. You are only charged for images where a plate is actually read. *** #### 🔌 API integration **Batch run:** ```bash curl -X POST "https://api.apify.com/v2/acts/acme-ai~plate-recognizer-ai/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"imageUrls":["https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg","https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg"]}' ``` **Standby (`POST /recognize`):** ```bash curl -X POST "https://acme-ai--plate-recognizer-ai.apify.actor/recognize" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ --compressed \ -d '{"imageUrls":["https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg","https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg"]}' ``` The token goes in the `Authorization: Bearer` header, never in the URL. *** ### Notes This Actor analyzes images you provide. You are responsible for having the right to process the images and any personal data they may contain (such as plates or people). # Actor input Schema ## `imageUrls` (type: `array`): Upload one or more vehicle images (JPG/JPEG, PNG, WebP) or paste public URLs. Up to 50 per run; send larger volumes via sequential calls. ## Actor input object example ```json { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg", "https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/6/6c/Florida_Antique_car_license_plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/d/d1/Illinois_Antique_Vehicle_License_Plate.jpg" ] } ``` # Actor output Schema ## `results` (type: `string`): Plate recognition results (one record per image). # 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 = { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg", "https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/6/6c/Florida_Antique_car_license_plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/d/d1/Illinois_Antique_Vehicle_License_Plate.jpg" ] }; // Run the Actor and wait for it to finish const run = await client.actor("acme-ai/plate-recognizer-ai").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 = { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg", "https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/6/6c/Florida_Antique_car_license_plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/d/d1/Illinois_Antique_Vehicle_License_Plate.jpg", ] } # Run the Actor and wait for it to finish run = client.actor("acme-ai/plate-recognizer-ai").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 '{ "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/6/66/Car_vehicle_israel_license_plate_%284893367447%29.jpg", "https://upload.wikimedia.org/wikipedia/commons/1/19/Michigan_Dealer_License_Plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/6/6c/Florida_Antique_car_license_plate.jpg", "https://upload.wikimedia.org/wikipedia/commons/d/d1/Illinois_Antique_Vehicle_License_Plate.jpg" ] }' | apify call acme-ai/plate-recognizer-ai --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=acme-ai/plate-recognizer-ai", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI License Plate Recognizer (OCR)", "description": "Read vehicle license plates from image URLs using AI vision. Returns plate, confidence, vehicle make/model/color, day/night and image metadata as JSON. Batch or real-time API.", "version": "1.0", "x-build-id": "59UMfy8REQtTZuLYm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/acme-ai~plate-recognizer-ai/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-acme-ai-plate-recognizer-ai", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/acme-ai~plate-recognizer-ai/runs": { "post": { "operationId": "runs-sync-acme-ai-plate-recognizer-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/acme-ai~plate-recognizer-ai/run-sync": { "post": { "operationId": "run-sync-acme-ai-plate-recognizer-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "imageUrls" ], "properties": { "imageUrls": { "title": "Images (upload or URLs)", "minItems": 1, "maxItems": 50, "type": "array", "description": "Upload one or more vehicle images (JPG/JPEG, PNG, WebP) or paste public URLs. Up to 50 per run; send larger volumes via sequential calls.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```