Design and implementation of license plate recognition system

Design and implementation of license plate recognition system

Project Overview

This project aims to design and implement a license plate recognition system. By using the license plate character data set for training, OpenCV, CNN (convolutional neural network) and PyQt5 technology are used to realize the preprocessing, position selection, positioning and character of the license plate image. Segmentation and final license plate recognition functionality.

technology stack

• OpenCV : Powerful computer vision library for image processing and analysis.
• CNN (Convolutional Neural Network) : Through deep learning models, efficient recognition of license plate characters is achieved.
• PyQt5 : Used to build graphical user interfaces (GUI) to provide a user-friendly interactive experience.

Features

1. License plate image preprocessing

• Image resizing
• Grayscale processing
• noise removal
• Contrast adjustment

2. Selection of license plate position

• Determine license plate location based on color and shape features
• Filter non-license plate areas

3. License plate positioning

• Use edge detection algorithm to find the edge of the license plate
• Perform morphological operations to extract the license plate area

4. License plate character segmentation

• Split license plate area characters into individual characters
• Recognize single characters through CNN model training

5. License plate recognition

• Combine the recognized characters into a complete license plate number
• Output the final license plate recognition results
  

Implementation steps

1. Dataset preparation : Use the license plate character data set for model training.
2. Image preprocessing : Use OpenCV for image size adjustment, grayscale processing, noise removal and other operations.
3. License plate position selection : Determine the license plate position based on color and shape characteristics.
4. License plate positioning : Use edge detection algorithm to find the edge of the license plate, and perform morphological operations to extract the license plate area.
5. Character segmentation : Split the license plate area characters into individual characters.
6. CNN training and character recognition : Use the CNN model to train and recognize single characters.
7. License plate number combination : Combine the recognized characters into a complete license plate number.
8. GUI design : Use PyQt5 to build a user-friendly graphical user interface to achieve system interaction.

Conclusion

By integrating OpenCV, CNN and PyQt5 technologies, this license plate recognition system can not only accurately locate and identify license plates, but also has a user-friendly interactive interface. It is hoped that this system can provide users with efficient and accurate license plate recognition services in practical applications. Welcome to leave a message for discussion and discuss the design and optimization of the license plate recognition system.