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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
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| 2026-07-24 | 447 |
| 2026-07-25 | 447 |
| 2026-07-28 | 447 |
| 2026-07-30 | 447 |
| 2026-08-06 | 447 |
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# 🚗 Persian License Plate Recognition System (PLPR) The 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. ## 🔍 Overview This system aims to tackle the unique challenges associated with Persian license plate detection and recognition, offering high accuracy and efficiency. It's well-suited for applications in traffic monitoring, automated vehicle identification, and similar fields. ## ✨ Key Features - **Advanced Detection**: Utilizes YOLOv5 models for high-accuracy license plate detection. - **Persian Character Recognition**: Custom-trained models ensure precise recognition of Persian characters. - **Real-Time Processing**: Capable of processing live video feeds in real-time. - **User-Friendly GUI**: Intuitive graphical user interface simplifies interactions with the system. --- <table> <tr> <td valign="top" width="50%"> <img src="/repo_images/parts.jpg" alt="explain main gui" style="max-width:300px;"> </td> <td valign="top" width="50%"> ## Main GUI Explanation - **1 Input View**: Shows video or camera feed. - **2 Detected Plate Highlight**: Draws rectangle around detected plates. - **3 Plate Image Display**: Shows captured image of the detected plate. - **4 Extracted Text**: Displays text recognized from the plate image. - **5 Owner Name**: Lists the registered owner's name. - **6 Plate Status**: Indicates if the plate is allowed, not allowed, or non-registered. - **7 Recent Entries Table**: Shows last 10 entries with options to add non-registered plates or view owner info. </td> </tr> </table> <table> <tr> <td valign="top" width="50%"> ## Resident Management <img src="/repo_images/people.jpg" alt="explain main resident management" style="max-width:300px;"> Focuses on maintaining and updating resident vehicle information, managing permissions for entry, and tracking resident vehicle movements within the premises. </td> <td valign="top" width="50%"> ## Entrance Management <img src="/repo_images/ent.png" alt="explain main entrance management" style="max-width:300px;"> Handles the regulation of vehicles entering and exiting the premises, ensuring only authorized vehicles gain access, and maintaining a log of all vehicle movements for security and administrative purposes. </td> </tr> </table> --- <table> <tr> <td valign="top" width="50%"> <img src="repo_images/detection_steps.png" alt="explain main flowchart" style="max-width:300px;"> </td> <td valign="top" width="50%"> ## Main Flowchart Explanation - **Start**: System initialization. - **Input Feed**: Receives image/video stream. - **Detect Plate**: YOLOv5 identifies license plates. - **Character Recognition**: Custom model recognizes Persian characters. - **Database Check**: Compares plate with database for status and owner. - **Display Results**: Shows detection and recognition results in GUI. - **Log Entry**: Updates recent entries table and database. - **End/Repeat**: Continues with new input or concludes operation. </td> </tr> <tr> <td colspan="2" valign="top"> **[Read the detailed flow in wiki](https://github.com/mtkarimi/smart-resident-guard/wiki/Main-Flow)** </td> </tr> </table> ### 💻 System Hardware Requirements To ensure optimal performance of the Persian License Plate Recognition System (PLPR), the following hardware specifications are recommended: - **Processor**: Intel Core i5 (8th Gen) or equivalent/higher. - **Memory**: 8 GB RAM or more. - **Graphics**: Dedicated GPU (NVIDIA GTX 1060 or equivalent) with at least 4 GB VRAM for efficient real-time processing and deep learning model computations. - **Storage**: SSD with at least 20 GB of free space for software, models, and datasets. - **Operating System**: Compatible with Windows 10/11, Linux (Ubuntu 18.04 or later
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e7a976fbcc0eaa89, topic:computer-vision