{"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","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":447,"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":"The PLPR system is designed to detect and recognize Persian license plates in images and video streams with real-time processing","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":"2026-08-01T00:00:13.66567+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"}],"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":"2026-08-01T00:00:12.877Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":775,"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":"2026-08-01T00:00:13.372Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-01T00:00:13.372Z"},"license_spdx":{"value":"GPL-3.0","source":"github.license","observed_at":"2026-08-01T00:00:13.372Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Requires an NVIDIA GPU with at least 4 GB of VRAM or equivalent for efficient real-time processing.","Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later), and macOS (10.14 Mojave or later).","The system must have an SSD with at least 20 GB of free space."],"min_ram_gb":8,"requires_docker":false},"constraints":{"min_ram_gb":8,"requires_docker":false},"when_to_use":["When you need software specifically calibrated for recognizing Persian license plate formats.","For applications requiring real-time processing of video streams or images for high accuracy in identifying vehicles registered within the Persian region."],"when_not_to_use":["If your project involves other types of license plates that are not specific to Persia, as the tool is optimized exclusively for Persian license plate formats.","For environments with hardware below the recommended specifications; this system requires a dedicated GPU and significant RAM for efficient real-time processing."],"source":"enrich:decision_facts","observed_at":"2026-07-16T22:12:42.604Z"},"constraint_facets":{"min_ram_gb":8,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Min 8 GB RAM; Requires an NVIDIA GPU with at least 4 GB of VRAM or equivalent for efficient real-time processing.; Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later), and macOS (10.14 Mojave or later).; The system must have an SSD with at least 20 GB of free space."},{"label":"Adopt for","value":"The persian-license-plate-recognition tool uses YOLOv5 and custom models to identify Persian license plates with real-time processing capabilities."}]}}