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\n
\n\n

Omni-LPR

\n\n[![Tests](https://img.shields.io/github/actions/workflow/status/habedi/omni-lpr/tests.yml?label=tests&style=flat&labelColor=333333&logo=github&logoColor=white)](https://github.com/habedi/omni-lpr/actions/workflows/tests.yml)\n[![Code Coverage](https://img.shields.io/codecov/c/github/habedi/omni-lpr?style=flat&label=coverage&labelColor=333333&logo=codecov&logoColor=white)](https://codecov.io/gh/habedi/omni-lpr)\n[![Code Quality](https://img.shields.io/codefactor/grade/github/habedi/omni-lpr?style=flat&label=code%20quality&labelColor=333333&logo=codefactor&logoColor=white)](https://www.codefactor.io/repository/github/habedi/omni-lpr)\n[![Python Version](https://img.shields.io/badge/python-3.10_--_3.13-3776ab?style=flat&labelColor=333333&logo=python&logoColor=white)](https://github.com/habedi/omni-lpr)\n[![PyPI](https://img.shields.io/pypi/v/omni-lpr?style=flat&labelColor=333333&logo=pypi&logoColor=white)](https://pypi.org/project/omni-lpr/)\n[![License](https://img.shields.io/badge/license-MIT-00acc1?style=flat&labelColor=333333&logo=open-source-initiative&logoColor=white)](https://github.com/habedi/omni-lpr/blob/main/LICENSE)\n
\n[![Documentation](https://img.shields.io/badge/docs-read-8ca0d7?style=flat&labelColor=282c34)](https://github.com/habedi/omni-lpr/tree/main/docs)\n[![Examples](https://img.shields.io/badge/examples-view-green?style=flat&labelColor=282c34)](https://github.com/habedi/omni-lpr/tree/main/examples)\n[![Docker Image (CPU)](https://img.shields.io/badge/Docker-CPU-007ec6?style=flat&logo=docker)](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-cpu)\n[![Docker Image (OpenVINO)](https://img.shields.io/badge/Docker-OpenVINO-007ec6?style=flat&logo=docker)](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-openvino)\n[![Docker Image (CUDA)](https://img.shields.io/badge/Docker-CUDA-007ec6?style=flat&logo=docker)](https://github.com/habedi/omni-lpr/pkgs/container/omni-lpr-cuda)\n\nA multi-interface (REST and MCP) server for automatic license plate recognition\n\n

\n\n---\n\nOmni-LPR is a self-hostable server that provides automatic license plate recognition (ALPR) capabilities via a REST API\nand the Model Context Protocol (MCP). It can be used both as a standalone ALPR microservice and as an ALPR toolbox for\nAI agents and large language models (LLMs).\n\n### Why Omni-LPR?\n\nUsing Omni-LPR can have the following benefits:\n\n- **Decoupling.** Your main application can be in any programming language. It doesn't need to be tangled up with Python\n or specific ML dependencies because the server handles all of that.\n\n- **Multiple Interfaces.** You aren't locked into one way of communicating. You can use a standard REST API from any\n app, or you can use MCP, which is designed for AI agent integration.\n\n- **Ready-to-Deploy.** You don't have to build it from scratch. There are pre-built Docker images that are easy to\n deploy and start using immediately.\n\n- **Hardware Acceleration.** The server is optimized for the hardware you have. It supports generic CPUs (ONNX), Intel\n CPUs (OpenVINO), and NVIDIA GPUs (CUDA).\n\n- **Asynchronous I/O.** It's built on Starlette, which means it has high-performance, non-blocking I/O. It can handle\n many concurrent requests without getting bogged down.\n\n- **Scalability.** Because it's a separate service, it can be scaled independently of your main application. If you\n suddenly need more ALPR power, you can scale Omni-LPR up without touching anything else.\n\n\nSee the [ROADMAP.md](https://github.com/habedi/omni-lpr/blob/main/ROADMAP.md) for the list of implemented and planned features.\n\n> [!IMPORTANT]\n> Omni-LPR is in early development, so bugs and breaking API changes are expected.\n> Please use the [issues page](https://github.com/habedi/omni-lpr/issues) to report bugs or request features.\n\n---\n\n### Quickstart\n\nYou can get started with Omni-LPR in a few minutes by following the steps described below.\n\n#### 1. Install the Server\n\nYou can install Omni-LPR using `pip`:\n\n```sh\npip install omni-lpr\n```\n\n#### 2. Start the Server\n\nWhen installed, start the server with a single command:\n\n```sh\nomni-lpr\n```\n\nBy default, the server will be listening on `http://127.0.0.1:8000`.\nYou can confirm it's running by accessing the health check endpoint:\n\n```sh\ncurl http://127.0.0.1:8000/api/health\n# Sample expected output: {\"status\": \"ok\", \"version\": \"0.3.4\"}\n```\n\n#### 3. Recognize a License Plate\n\nNow you can make a request to recognize a license plate from an image.\nThe example below uses a publicly available image URL.\n\n```sh\ncurl -X POST \\\n -H \"Content-Type: application/json\" \\\n -d '{\"path\": \"https://www.olavsplates.com/foto_n/n_cx11111.jpg\"}' \\\n http://127.0.0.1:8000/api/v1/tools/detect_and_recognize_plate_from_path/invoke\n```\n\nYou should receive a JSON response with the detected license plate information.\n\n### Usage\n\nOmni-LPR exposes its capabilities as \"tools\" that can be called via a REST API or over the MCP.\n\n#### Available Tools\n\nThe server provides tools for listing models, recognizing plates from image data, and recognizing plates from a path.\n\n- `list_models`: Lists the available detector and OCR models.\n\n- **Tools that process image data** (provided as Base64 or file upload):\n - `recognize_plate`: Recognizes text from a pre-cropped license plate image.\n - `detect_and_recognize_plate`: Detects and recognizes all license plates in a full image.\n\n- **Tools that process an image path** (a URL or local file path):\n - `recognize_plate_from_path`: Recognizes text from a pre-cropped license plate image at a given path.\n - `detect_and_recognize_plate_from_path`: Detects and recognizes plates in a full image at a given path.\n\nFor more details on how to use the different tools and provide image data, please see the\n[API Documentation](https://github.com/habedi/omni-lpr/blob/main/docs/README.md).\n\n#### REST API\n\nThe REST API provides a standard way to interact with the server. All tool endpoints are available under the `/api/v1`\nprefix. Once the server is running, you can access interactive API documentation in the Swagger UI\nat http://127.0.0.1:8000/api/v1/apidoc/swagger.\n\n#### MCP Interface\n\nThe server also exposes its tools over the MCP for integration with AI agents and LLMs. The MCP endpoint is available at\nhttp://127.0.0.1:8000/mcp/, via streamable HTTP.\n\nYou can use a tool like [MCP Inspector](https://github.com/modelcontextprotocol/inspector) to explore the available MCP\ntools.\n\n

\n\n### Integration\n\nYou can connect any client that supports the MCP protocol to the server.\nThe following examples show how to use the server with [LM Studio](https://lmstudio.ai/).\n\n#### LM Studio Configuration\n\n```json\n{\n \"mcpServers\": {\n \"omni-lpr-local\": {\n \"url\": \"http://127.0.0.1:8000/mcp/\"\n }\n }\n}\n```\n\n#### Tool Usage Examples\n\nThe screenshot of using the `list_models` tool in LM Studio to list the available models for the APLR.\n\n

\n\nThe screenshot below shows using the `detect_and_recognize_plate_from_path` tool in LM Studio to detect and recognize\nthe license plate from an [image available on the web](https://www.olavsplates.com/foto_n/n_cx11111.jpg).\n\n

\n\n---\n\n### Documentation\n\nOmni-LPR documentation is available [here](https://github.com/habedi/omni-lpr/blob/main/docs).\n\n#### Examples\n\nCheck out the [examples](https://github.com/habedi/omni-lpr/blob/main/examples) directory for usage examples.\n\n---\n\n### Contributing\n\nContributions are always welcome!\nPlease see [CONTRIBUTING.md](https://github.com/habedi/omni-lpr/blob/main/CONTRIBUTING.md) for details on how to get started.\n\n### License\n\nOmni-LPR is licensed under the MIT License (see [LICENSE](https://github.com/habedi/omni-lpr/blob/main/LICENSE)).\n\n### Acknowledgements\n\n- This project uses the awesome [fast-plate-ocr](https://github.com/ankandrew/fast-plate-ocr)\n and [fast-alpr](https://github.com/ankandrew/fast-alpr) Python libraries.\n- The project logo is from [SVG Repo](https://www.svgrepo.com/svg/237124/license-plate-number).\n\n\n\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays",0,"iconType","image","upstream",{"_106":107,"_108":109,"_110":111,"_112":113,"_114":-5,"_115":116},"requiredSecrets",[],"tags",[97,98,99,100,101,102,103,104,105],"assets",[],"stats",{"_95":82,"_96":82},"ratingAvg","reviewCount","automatic-license-plate-recognition","computer-vision","deep-learning","generative-ai","license-plate-recognition","mlops","ocr","python","rest-api","topics",[97,98,99,100,101,117,102,103,104,105],"license","MIT","language","Python","registryName","io.github.habedi/omni-lpr","homepage","packages",[],"mcp-server","ENV",{"_120":121,"_122":123,"_124":125},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]