This project aims to develop a License Plate Recognition (LPR) system that can accurately detect and recognize license plates in images or video streams. The system utilizes computer vision and deep learning techniques to achieve robust and efficient license plate recognition.
License Plate Recognition (LPR) is a technology that automates the process of detecting and reading license plates from images or video sources. It has various applications, including traffic management, parking systems, and law enforcement. This project focuses on developing an LPR system that can handle different license plate formats, handle variations in lighting conditions, and accurately recognize license plate characters.
- License plate detection: The system can detect license plate regions in images or video frames.
- Character segmentation: It segments the characters in the license plate for individual recognition.
- Character recognition: Utilizes deep learning models to recognize the characters on the license plate accurately.
- Multiple license plate formats: Supports recognition of various license plate formats, including standard and non-standard ones.
- Real-time processing: The system is optimized for real-time processing, making it suitable for applications such as video surveillance or traffic monitoring.
To install and run the LPR system, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/lpr-system.git - Install the required dependencies:
pip install -r requirements.txt - Download the pre-trained models: [model-link]
- Place the pre-trained models in the appropriate directory.
- [Additional installation steps, if any]
[Explain how to use the LPR system, including any command-line options or configuration files. Provide examples and sample code if necessary.]
python lpr_system.py --input image.jpg
The LPR system was trained on a dataset of license plate images collected from various sources. The dataset contains different license plate formats, including standard and non-standard plates, captured under various lighting conditions. However, due to data privacy restrictions, the dataset used for training is not publicly available.
[If applicable, provide instructions on training the LPR system using the provided dataset or any additional datasets.]
- Preprocess the dataset: [Describe any preprocessing steps, such as resizing, normalization, or augmentation.]
- Train the LPR model: [Provide the command or script to train the model with the dataset.]
- Evaluate the trained model: [Describe the evaluation metrics used to assess the model's performance.]
[Explain how to evaluate the performance of the LPR system, including any evaluation scripts or metrics used.]
[Specify the license under which the LPR system is released.]
The project is released under the MIT License.
[Include any other relevant sections or information as needed.]
Feel free to customize this README file based on your specific project requirements and add any additional sections that are relevant to your LPR system.