{"data":{"slug":"truthofmatthew-persian-license-plate-recognition","name":"persian-license-plate-recognition","tagline":"PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.","github_url":"https://github.com/truthofmatthew/persian-license-plate-recognition","owner":"truthofmatthew","repo":"persian-license-plate-recognition","owner_avatar_url":"https://avatars.githubusercontent.com/u/3770570?v=4","primary_language":"Python","stars":446,"forks":126,"topics":["ai","computer-vision","image-processing","license-plate-recognition","machine-learning","persian-license-plate","python","vehicle-identification","yolov5"],"archived":false,"github_pushed_at":"2024-06-16T14:42:49+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/truthofmatthew-persian-license-plate-recognition","markdown_url":"https://www.graphcanon.com/tools/truthofmatthew-persian-license-plate-recognition.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/truthofmatthew-persian-license-plate-recognition","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=truthofmatthew-persian-license-plate-recognition","description":"PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.","homepage_url":null,"license":"GPL-3.0","open_issues":7,"watchers":10,"ai_summary":null,"readme_excerpt":"# 🚗 Persian License Plate Recognition System (PLPR)\n\nThe Persian License Plate Recognition (PLPR) system is a state-of-the-art solution designed for detecting and recognizing Persian license plates in images and video streams. Leveraging advanced deep learning models and a user-friendly interface, it ensures reliable performance across different scenarios.\n\n---\n\n### 💻 System Hardware Requirements\n\nTo ensure optimal performance of the Persian License Plate Recognition System (PLPR), the following hardware specifications are recommended:\n\n- **Processor**: Intel Core i5 (8th Gen) or equivalent/higher.\n- **Memory**: 8 GB RAM or more.\n- **Graphics**: Dedicated GPU (NVIDIA GTX 1060 or equivalent) with at least 4 GB VRAM for efficient real-time processing and deep learning model computations.\n- **Storage**: SSD with at least 20 GB of free space for software, models, and datasets.\n- **Operating System**: Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later), and macOS (10.14 Mojave or later).\n\nThese specifications are designed to handle the computational demands of advanced deep learning models, real-time video processing, and high-volume data management integral to the PLPR system. Adjustments may be necessary based on specific deployment scenarios and performance expectations.\n\n---\n\n### 🔧 Installation\n\n1. Clone the repository and navigate to its directory:\n   ```bash\n   git clone https://github.com/mtkarimi/smart-resident-guard.git\n   cd smart-resident-guard\n   ```\n2. Install the required Python packages:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n---\n\n## 📄 License\n\nGPL-3.0. See the [LICENSE](LICENSE) file for details. It means you can:\n- Share Source Code: If you distribute binaries or modified versions, you must make the source code available under GPL-3.\n- License: Must keep and apply GPL-3 to the modified work.\n- State Modifications: If modified, must disclose that it was changed.\n  \n---","github_created_at":"2024-02-20T21:06:32+00:00","created_at":"2026-07-11T12:29:35.704633+00:00","updated_at":"2026-07-11T12:29:44.890724+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"image-processing","name":"image processing"},{"slug":"license-plate-recognition","name":"license-plate-recognition"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"persian-license-plate","name":"persian-license-plate"},{"slug":"python","name":"python"},{"slug":"vehicle-identification","name":"vehicle-identification"}],"trust":{"provenance":{"is_fork":false,"github_id":760887878,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:29:36.380Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":754,"last_release_at":null},"security_summary":{"status":"ok","scanner":"osv@v1","low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:29:40.027Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:29:39.628Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-07-11T12:29:39.628Z"},"license_spdx":{"value":"GPL-3.0","source":"github.license","observed_at":"2026-07-11T12:29:39.628Z"}}}}